{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eegpnTbImFUE"
   },
   "source": [
    "First, download datasets (It will take a few minutes. You can comment out some of the files if you want a smaller dataset):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 7633
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 163801,
     "status": "ok",
     "timestamp": 1525477369898,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "NSi52lWjPNN4",
    "outputId": "406f5950-aad6-4eff-d231-26cd21130e9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-05-04 23:40:07--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S01T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S01T.mat [following]\n",
      "--2018-05-04 23:40:08--  https://lampx.tugraz.at/~bci/database/002-2014/S01T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39794870 (38M)\n",
      "Saving to: ‘S01T.mat’\n",
      "\n",
      "S01T.mat            100%[===================>]  37.95M  9.89MB/s    in 4.3s    \n",
      "\n",
      "2018-05-04 23:40:13 (8.83 MB/s) - ‘S01T.mat’ saved [39794870/39794870]\n",
      "\n",
      "--2018-05-04 23:40:14--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S01E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S01E.mat [following]\n",
      "--2018-05-04 23:40:14--  https://lampx.tugraz.at/~bci/database/002-2014/S01E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23769588 (23M)\n",
      "Saving to: ‘S01E.mat’\n",
      "\n",
      "S01E.mat            100%[===================>]  22.67M  6.61MB/s    in 3.4s    \n",
      "\n",
      "2018-05-04 23:40:18 (6.61 MB/s) - ‘S01E.mat’ saved [23769588/23769588]\n",
      "\n",
      "--2018-05-04 23:40:19--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S02T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S02T.mat [following]\n",
      "--2018-05-04 23:40:20--  https://lampx.tugraz.at/~bci/database/002-2014/S02T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 38364523 (37M)\n",
      "Saving to: ‘S02T.mat’\n",
      "\n",
      "S02T.mat             33%[=====>              ]  12.40M  4.09MB/s    eta 6s     S02T.mat            100%[===================>]  36.59M  9.45MB/s    in 4.1s    \n",
      "\n",
      "2018-05-04 23:40:24 (8.91 MB/s) - ‘S02T.mat’ saved [38364523/38364523]\n",
      "\n",
      "--2018-05-04 23:40:25--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S02E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S02E.mat [following]\n",
      "--2018-05-04 23:40:26--  https://lampx.tugraz.at/~bci/database/002-2014/S02E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 22998442 (22M)\n",
      "Saving to: ‘S02E.mat’\n",
      "\n",
      "S02E.mat            100%[===================>]  21.93M  6.55MB/s    in 3.3s    \n",
      "\n",
      "2018-05-04 23:40:30 (6.55 MB/s) - ‘S02E.mat’ saved [22998442/22998442]\n",
      "\n",
      "--2018-05-04 23:40:31--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S03T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S03T.mat [following]\n",
      "--2018-05-04 23:40:31--  https://lampx.tugraz.at/~bci/database/002-2014/S03T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39739945 (38M)\n",
      "Saving to: ‘S03T.mat’\n",
      "\n",
      "S03T.mat            100%[===================>]  37.90M  9.95MB/s    in 4.3s    \n",
      "\n",
      "2018-05-04 23:40:36 (8.87 MB/s) - ‘S03T.mat’ saved [39739945/39739945]\n",
      "\n",
      "--2018-05-04 23:40:37--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S03E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S03E.mat [following]\n",
      "--2018-05-04 23:40:37--  https://lampx.tugraz.at/~bci/database/002-2014/S03E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23797275 (23M)\n",
      "Saving to: ‘S03E.mat’\n",
      "\n",
      "S03E.mat             14%[=>                  ]   3.21M  1.52MB/s               S03E.mat            100%[===================>]  22.69M  7.08MB/s    in 3.2s    \n",
      "\n",
      "2018-05-04 23:40:41 (7.08 MB/s) - ‘S03E.mat’ saved [23797275/23797275]\n",
      "\n",
      "--2018-05-04 23:40:42--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S04T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S04T.mat [following]\n",
      "--2018-05-04 23:40:42--  https://lampx.tugraz.at/~bci/database/002-2014/S04T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39607931 (38M)\n",
      "Saving to: ‘S04T.mat’\n",
      "\n",
      "S04T.mat            100%[===================>]  37.77M  9.54MB/s    in 4.2s    \n",
      "\n",
      "2018-05-04 23:40:47 (9.01 MB/s) - ‘S04T.mat’ saved [39607931/39607931]\n",
      "\n",
      "--2018-05-04 23:40:48--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S04E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S04E.mat [following]\n",
      "--2018-05-04 23:40:48--  https://lampx.tugraz.at/~bci/database/002-2014/S04E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23752764 (23M)\n",
      "Saving to: ‘S04E.mat’\n",
      "\n",
      "S04E.mat            100%[===================>]  22.65M  6.53MB/s    in 3.5s    \n",
      "\n",
      "2018-05-04 23:40:52 (6.53 MB/s) - ‘S04E.mat’ saved [23752764/23752764]\n",
      "\n",
      "--2018-05-04 23:40:53--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S05T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S05T.mat [following]\n",
      "--2018-05-04 23:40:54--  https://lampx.tugraz.at/~bci/database/002-2014/S05T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39091212 (37M)\n",
      "Saving to: ‘S05T.mat’\n",
      "\n",
      "S05T.mat              7%[>                   ]   2.63M  1.13MB/s               S05T.mat            100%[===================>]  37.28M  10.0MB/s    in 4.2s    \n",
      "\n",
      "2018-05-04 23:40:58 (8.92 MB/s) - ‘S05T.mat’ saved [39091212/39091212]\n",
      "\n",
      "--2018-05-04 23:40:59--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S05E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S05E.mat [following]\n",
      "--2018-05-04 23:41:00--  https://lampx.tugraz.at/~bci/database/002-2014/S05E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23628634 (23M)\n",
      "Saving to: ‘S05E.mat’\n",
      "\n",
      "S05E.mat            100%[===================>]  22.53M  6.48MB/s    in 3.5s    \n",
      "\n",
      "2018-05-04 23:41:04 (6.48 MB/s) - ‘S05E.mat’ saved [23628634/23628634]\n",
      "\n",
      "--2018-05-04 23:41:05--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S06T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S06T.mat [following]\n",
      "--2018-05-04 23:41:05--  https://lampx.tugraz.at/~bci/database/002-2014/S06T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 40027626 (38M)\n",
      "Saving to: ‘S06T.mat’\n",
      "\n",
      "S06T.mat            100%[===================>]  38.17M  9.87MB/s    in 4.3s    \n",
      "\n",
      "2018-05-04 23:41:10 (8.82 MB/s) - ‘S06T.mat’ saved [40027626/40027626]\n",
      "\n",
      "--2018-05-04 23:41:11--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S06E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S06E.mat [following]\n",
      "--2018-05-04 23:41:11--  https://lampx.tugraz.at/~bci/database/002-2014/S06E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23850651 (23M)\n",
      "Saving to: ‘S06E.mat’\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S06E.mat             11%[=>                  ]   2.73M  1.30MB/s               S06E.mat            100%[===================>]  22.75M  6.99MB/s    in 3.3s    \n",
      "\n",
      "2018-05-04 23:41:15 (6.99 MB/s) - ‘S06E.mat’ saved [23850651/23850651]\n",
      "\n",
      "--2018-05-04 23:41:16--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S07T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S07T.mat [following]\n",
      "--2018-05-04 23:41:16--  https://lampx.tugraz.at/~bci/database/002-2014/S07T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 40261257 (38M)\n",
      "Saving to: ‘S07T.mat’\n",
      "\n",
      "S07T.mat            100%[===================>]  38.40M  9.67MB/s    in 4.0s    \n",
      "\n",
      "2018-05-04 23:41:21 (9.67 MB/s) - ‘S07T.mat’ saved [40261257/40261257]\n",
      "\n",
      "--2018-05-04 23:41:22--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S07E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S07E.mat [following]\n",
      "--2018-05-04 23:41:22--  https://lampx.tugraz.at/~bci/database/002-2014/S07E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23997847 (23M)\n",
      "Saving to: ‘S07E.mat’\n",
      "\n",
      "S07E.mat            100%[===================>]  22.89M  6.27MB/s    in 3.6s    \n",
      "\n",
      "2018-05-04 23:41:27 (6.27 MB/s) - ‘S07E.mat’ saved [23997847/23997847]\n",
      "\n",
      "--2018-05-04 23:41:28--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S08T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S08T.mat [following]\n",
      "--2018-05-04 23:41:28--  https://lampx.tugraz.at/~bci/database/002-2014/S08T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39735612 (38M)\n",
      "Saving to: ‘S08T.mat’\n",
      "\n",
      "S08T.mat              7%[>                   ]   2.95M  1.26MB/s               S08T.mat            100%[===================>]  37.89M  9.63MB/s    in 4.2s    \n",
      "\n",
      "2018-05-04 23:41:33 (9.09 MB/s) - ‘S08T.mat’ saved [39735612/39735612]\n",
      "\n",
      "--2018-05-04 23:41:34--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S08E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S08E.mat [following]\n",
      "--2018-05-04 23:41:34--  https://lampx.tugraz.at/~bci/database/002-2014/S08E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23791571 (23M)\n",
      "Saving to: ‘S08E.mat’\n",
      "\n",
      "S08E.mat            100%[===================>]  22.69M  6.89MB/s    in 3.3s    \n",
      "\n",
      "2018-05-04 23:41:38 (6.89 MB/s) - ‘S08E.mat’ saved [23791571/23791571]\n",
      "\n",
      "--2018-05-04 23:41:39--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S09T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S09T.mat [following]\n",
      "--2018-05-04 23:41:39--  https://lampx.tugraz.at/~bci/database/002-2014/S09T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39805150 (38M)\n",
      "Saving to: ‘S09T.mat’\n",
      "\n",
      "S09T.mat            100%[===================>]  37.96M  9.61MB/s    in 4.2s    \n",
      "\n",
      "2018-05-04 23:41:44 (9.08 MB/s) - ‘S09T.mat’ saved [39805150/39805150]\n",
      "\n",
      "--2018-05-04 23:41:45--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S09E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S09E.mat [following]\n",
      "--2018-05-04 23:41:45--  https://lampx.tugraz.at/~bci/database/002-2014/S09E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23610622 (23M)\n",
      "Saving to: ‘S09E.mat’\n",
      "\n",
      "S09E.mat              8%[>                   ]   2.01M   982KB/s               S09E.mat            100%[===================>]  22.52M  6.66MB/s    in 3.4s    \n",
      "\n",
      "2018-05-04 23:41:49 (6.66 MB/s) - ‘S09E.mat’ saved [23610622/23610622]\n",
      "\n",
      "--2018-05-04 23:41:50--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S10T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S10T.mat [following]\n",
      "--2018-05-04 23:41:51--  https://lampx.tugraz.at/~bci/database/002-2014/S10T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39871971 (38M)\n",
      "Saving to: ‘S10T.mat’\n",
      "\n",
      "S10T.mat            100%[===================>]  38.02M  10.3MB/s    in 4.2s    \n",
      "\n",
      "2018-05-04 23:41:55 (9.13 MB/s) - ‘S10T.mat’ saved [39871971/39871971]\n",
      "\n",
      "--2018-05-04 23:41:56--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S10E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S10E.mat [following]\n",
      "--2018-05-04 23:41:57--  https://lampx.tugraz.at/~bci/database/002-2014/S10E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23750112 (23M)\n",
      "Saving to: ‘S10E.mat’\n",
      "\n",
      "S10E.mat            100%[===================>]  22.65M  6.49MB/s    in 3.5s    \n",
      "\n",
      "2018-05-04 23:42:01 (6.49 MB/s) - ‘S10E.mat’ saved [23750112/23750112]\n",
      "\n",
      "--2018-05-04 23:42:02--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S11T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S11T.mat [following]\n",
      "--2018-05-04 23:42:02--  https://lampx.tugraz.at/~bci/database/002-2014/S11T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 40140184 (38M)\n",
      "Saving to: ‘S11T.mat’\n",
      "\n",
      "S11T.mat              5%[>                   ]   1.96M   942KB/s               S11T.mat            100%[===================>]  38.28M  9.48MB/s    in 6.6s    \n",
      "\n",
      "2018-05-04 23:42:09 (5.83 MB/s) - ‘S11T.mat’ saved [40140184/40140184]\n",
      "\n",
      "--2018-05-04 23:42:10--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S11E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S11E.mat [following]\n",
      "--2018-05-04 23:42:11--  https://lampx.tugraz.at/~bci/database/002-2014/S11E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 24171700 (23M)\n",
      "Saving to: ‘S11E.mat’\n",
      "\n",
      "S11E.mat            100%[===================>]  23.05M  6.72MB/s    in 3.4s    \n",
      "\n",
      "2018-05-04 23:42:14 (6.72 MB/s) - ‘S11E.mat’ saved [24171700/24171700]\n",
      "\n",
      "--2018-05-04 23:42:16--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S12T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S12T.mat [following]\n",
      "--2018-05-04 23:42:16--  https://lampx.tugraz.at/~bci/database/002-2014/S12T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 40093093 (38M)\n",
      "Saving to: ‘S12T.mat’\n",
      "\n",
      "S12T.mat            100%[===================>]  38.24M  10.1MB/s    in 4.0s    \n",
      "\n",
      "2018-05-04 23:42:20 (9.49 MB/s) - ‘S12T.mat’ saved [40093093/40093093]\n",
      "\n",
      "--2018-05-04 23:42:22--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S12E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S12E.mat [following]\n",
      "--2018-05-04 23:42:22--  https://lampx.tugraz.at/~bci/database/002-2014/S12E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23905075 (23M)\n",
      "Saving to: ‘S12E.mat’\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S12E.mat              5%[>                   ]   1.35M   740KB/s               S12E.mat            100%[===================>]  22.80M  5.67MB/s    in 4.0s    \n",
      "\n",
      "2018-05-04 23:42:26 (5.67 MB/s) - ‘S12E.mat’ saved [23905075/23905075]\n",
      "\n",
      "--2018-05-04 23:42:28--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S13T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S13T.mat [following]\n",
      "--2018-05-04 23:42:28--  https://lampx.tugraz.at/~bci/database/002-2014/S13T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39819174 (38M)\n",
      "Saving to: ‘S13T.mat’\n",
      "\n",
      "S13T.mat            100%[===================>]  37.97M  10.0MB/s    in 4.3s    \n",
      "\n",
      "2018-05-04 23:42:33 (8.91 MB/s) - ‘S13T.mat’ saved [39819174/39819174]\n",
      "\n",
      "--2018-05-04 23:42:34--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S13E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S13E.mat [following]\n",
      "--2018-05-04 23:42:34--  https://lampx.tugraz.at/~bci/database/002-2014/S13E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23669359 (23M)\n",
      "Saving to: ‘S13E.mat’\n",
      "\n",
      "S13E.mat            100%[===================>]  22.57M  6.86MB/s    in 3.3s    \n",
      "\n",
      "2018-05-04 23:42:38 (6.86 MB/s) - ‘S13E.mat’ saved [23669359/23669359]\n",
      "\n",
      "--2018-05-04 23:42:39--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S14T.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S14T.mat [following]\n",
      "--2018-05-04 23:42:39--  https://lampx.tugraz.at/~bci/database/002-2014/S14T.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 39859134 (38M)\n",
      "Saving to: ‘S14T.mat’\n",
      "\n",
      "S14T.mat              7%[>                   ]   2.79M  1.17MB/s               S14T.mat            100%[===================>]  38.01M  9.45MB/s    in 4.3s    \n",
      "\n",
      "2018-05-04 23:42:44 (8.92 MB/s) - ‘S14T.mat’ saved [39859134/39859134]\n",
      "\n",
      "--2018-05-04 23:42:45--  http://bnci-horizon-2020.eu/database/data-sets/002-2014/S14E.mat\n",
      "Resolving bnci-horizon-2020.eu (bnci-horizon-2020.eu)... 91.227.204.35\n",
      "Connecting to bnci-horizon-2020.eu (bnci-horizon-2020.eu)|91.227.204.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://lampx.tugraz.at/~bci/database/002-2014/S14E.mat [following]\n",
      "--2018-05-04 23:42:45--  https://lampx.tugraz.at/~bci/database/002-2014/S14E.mat\n",
      "Resolving lampx.tugraz.at (lampx.tugraz.at)... 129.27.124.207\n",
      "Connecting to lampx.tugraz.at (lampx.tugraz.at)|129.27.124.207|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23970887 (23M)\n",
      "Saving to: ‘S14E.mat’\n",
      "\n",
      "S14E.mat            100%[===================>]  22.86M  6.96MB/s    in 3.3s    \n",
      "\n",
      "2018-05-04 23:42:49 (6.96 MB/s) - ‘S14E.mat’ saved [23970887/23970887]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S01T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S01E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S02T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S02E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S03T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S03E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S04T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S04E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S05T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S05E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S06T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S06E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S07T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S07E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S08T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S08E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S09T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S09E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S10T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S10E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S11T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S11E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S12T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S12E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S13T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S13E.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S14T.mat\n",
    "!wget http://bnci-horizon-2020.eu/database/data-sets/002-2014/S14E.mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dur3RW3pmWfv"
   },
   "source": [
    "Move files to a separate folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "Erg5PSb_PXi2"
   },
   "outputs": [],
   "source": [
    "!mkdir BBCIData\n",
    "!mv *.mat BBCIData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v7CK51BEmbN5"
   },
   "source": [
    "Install dependencies (braindecode & pytorch):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "hxBTOWbvPjpP"
   },
   "outputs": [],
   "source": [
    "!pip install braindecode -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "LxVQ9HYqQCSO"
   },
   "outputs": [],
   "source": [
    "# http://pytorch.org/\n",
    "from os import path\n",
    "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
    "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
    "\n",
    "accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'\n",
    "\n",
    "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ejLdMTpjmiRY"
   },
   "source": [
    "Now, let's load data.\n",
    "\n",
    "We read the file for the desired subject, and parse the data to extract:\n",
    "- samplingRate\n",
    "- trialLength\n",
    "- X, a M x N x K matrix, which stands for trial x chan x samples\n",
    "    - the actual values are 160 x 15 x 2560\n",
    "- y, a M vector containing the labels {0,1}\n",
    "\n",
    "ref: Dataset description: https://lampx.tugraz.at/~bci/database/002-2014/description.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11881,
     "status": "ok",
     "timestamp": 1525477614849,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "DpLUMPuqN5CZ",
    "outputId": "a78da791-5dd8-46b9-e4e8-20a1ee97de14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2240, 15, 3584)\n",
      "(2240,)\n"
     ]
    }
   ],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "# prepare data containers\n",
    "y = []\n",
    "X = []\n",
    "\n",
    "folder = \"../BBCIData\"\n",
    "\n",
    "for f in listdir(folder):\n",
    "    # read file\n",
    "    d1T = sio.loadmat(folder + \"/\" + f)\n",
    "    \n",
    "    samplingRate = d1T['data'][0][0][0][0][3][0][0]\n",
    "    trialLength = 7*samplingRate\n",
    "\n",
    "\n",
    "    # run through all training runs\n",
    "    for run in range(len(d1T['data'][0])):\n",
    "        y.append(d1T['data'][0][run][0][0][2][0]) # labels\n",
    "        timestamps = d1T['data'][0][run][0][0][1][0] # timestamps\n",
    "        rawData = d1T['data'][0][run][0][0][0].transpose() # chan x data\n",
    "\n",
    "        # parse out data based on timestamps\n",
    "        for start in timestamps:\n",
    "            end = start + trialLength\n",
    "            X.append(rawData[:,start:end]) #15 x 2560\n",
    "\n",
    "    del rawData\n",
    "    del d1T\n",
    "\n",
    "# arrange data into num7py arrays\n",
    "# also torch expect float32 for samples\n",
    "# and int64 for labels {0,1}\n",
    "X = np.array(X).astype(np.float32)\n",
    "y = (np.array(y).flatten()-1).astype(np.int64)\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ICtnURjeo3vf"
   },
   "source": [
    "Load the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "6PUAO5bSPfN3"
   },
   "outputs": [],
   "source": [
    "from braindecode.datautil.signal_target import SignalAndTarget\n",
    "from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
    "from torch import nn\n",
    "from braindecode.torch_ext.util import set_random_seeds    \n",
    "from torch import optim\n",
    "import torch\n",
    "\n",
    "idx = np.random.permutation(X.shape[0])\n",
    "\n",
    "X = X[idx,:,:]\n",
    "y = y[idx]\n",
    "\n",
    "nb_train_trials = int(np.floor(7/8*X.shape[0]))\n",
    "\n",
    "\n",
    "train_set = SignalAndTarget(X[:nb_train_trials], y=y[:nb_train_trials])\n",
    "test_set = SignalAndTarget(X[nb_train_trials:], y=y[nb_train_trials:])\n",
    "\n",
    "# Set if you want to use GPU\n",
    "# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
    "cuda = torch.cuda.is_available()\n",
    "set_random_seeds(seed=20180505, cuda=cuda)\n",
    "n_classes = 2\n",
    "in_chans = train_set.X.shape[1]\n",
    "# final_conv_length = auto ensures we only get a single output in the time dimension\n",
    "model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes,\n",
    "                        input_time_length=train_set.X.shape[2],\n",
    "                        \n",
    "                        n_filters_time=10,\n",
    "                        filter_time_length=75,\n",
    "                        n_filters_spat=5,\n",
    "                        pool_time_length=60,\n",
    "                        pool_time_stride=30,\n",
    "                        \n",
    "                        #n_filters_time=10,\n",
    "                        #filter_time_length=90,\n",
    "                        #n_filters_spat=1,\n",
    "                        #pool_time_length=45,\n",
    "                        #pool_time_stride=15,\n",
    "                        \n",
    "                        final_conv_length='auto'\n",
    "                        ).create_network()\n",
    "if cuda:\n",
    "    model.cuda()\n",
    "    \n",
    "optimizer = optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D5De8ILppIB6"
   },
   "source": [
    "Load optimizer. You can find hyperparameters in the link below.  \n",
    "http://pytorch.org/docs/master/optim.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lvliRV3dpazA"
   },
   "source": [
    "Finally start training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 13651
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 655282,
     "status": "ok",
     "timestamp": 1525487932689,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "qhG6M1RZQWAe",
    "outputId": "b49a62f3-12d1-4ec2-e4f1-2281d9c782f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Train  Loss: 0.78019\n",
      "Train  Accuracy: 52.1%\n",
      "Test   Loss: 0.76632\n",
      "Test   Accuracy: 50.7%\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-01a3ebfe2447>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet_in\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;31m# Compute the loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnll_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnet_target\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0;31m# Do the backpropagation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "from braindecode.torch_ext.util import np_to_var, var_to_np\n",
    "from braindecode.datautil.iterators import get_balanced_batches\n",
    "import torch.nn.functional as F\n",
    "from numpy.random import RandomState\n",
    "rng = RandomState(None)\n",
    "#rng = RandomState((2017,6,30))\n",
    "\n",
    "nb_epoch = 160\n",
    "loss_rec = np.zeros((nb_epoch,2))\n",
    "accuracy_rec = np.zeros((nb_epoch,2))\n",
    "\n",
    "\n",
    "for i_epoch in range(nb_epoch):\n",
    "    \n",
    "    # get a set of balanced batches\n",
    "    i_trials_in_batch = get_balanced_batches(len(train_set.X), rng, shuffle=True,\n",
    "                                            batch_size=32)\n",
    "    \n",
    "    \n",
    "    # Set model to training mode\n",
    "    model.train()\n",
    "    \n",
    "    # go through all batches\n",
    "    for i_trials in i_trials_in_batch:\n",
    "        # Have to add empty fourth dimension to X\n",
    "        batch_X = train_set.X[i_trials][:,:,:,None]\n",
    "        batch_y = train_set.y[i_trials]\n",
    "        net_in = np_to_var(batch_X)\n",
    "        if cuda:\n",
    "            net_in = net_in.cuda()\n",
    "        net_target = np_to_var(batch_y)\n",
    "        if cuda:\n",
    "            net_target = net_target.cuda()\n",
    "        # Remove gradients of last backward pass from all parameters\n",
    "        optimizer.zero_grad()\n",
    "        # Compute outputs of the network\n",
    "        outputs = model(net_in)\n",
    "        # Compute the loss\n",
    "        loss = F.nll_loss(outputs, net_target)\n",
    "        # Do the backpropagation\n",
    "        loss.backward()\n",
    "        # Update parameters with the optimizer\n",
    "        optimizer.step()\n",
    "\n",
    "    # Print some statistics each epoch\n",
    "    model.eval()\n",
    "    print(\"Epoch {:d}\".format(i_epoch))\n",
    "    \n",
    "    sets = {'Train' : 0, 'Test' : 1}\n",
    "    for setname, dataset in (('Train', train_set), ('Test', test_set)):\n",
    "        i_trials_in_batch = get_balanced_batches(len(dataset.X), rng, batch_size=32, shuffle=False)\n",
    "        outputs = []\n",
    "        net_targets = []\n",
    "        for i_trials in i_trials_in_batch:\n",
    "            batch_X = dataset.X[i_trials][:,:,:,None]\n",
    "            batch_y = dataset.y[i_trials]\n",
    "            \n",
    "            net_in = np_to_var(batch_X)\n",
    "            if cuda:\n",
    "                net_in = net_in.cuda()\n",
    "            net_target = np_to_var(batch_y)\n",
    "            if cuda:\n",
    "                net_target = net_target.cuda()\n",
    "            net_target = var_to_np(net_target)\n",
    "            output = var_to_np(model(net_in))\n",
    "            outputs.append(output)\n",
    "            net_targets.append(net_target)\n",
    "        net_targets = np_to_var(np.concatenate(net_targets))\n",
    "        outputs = np_to_var(np.concatenate(outputs))\n",
    "        loss = F.nll_loss(outputs, net_targets)\n",
    " \n",
    "        print(\"{:6s} Loss: {:.5f}\".format(\n",
    "            setname, float(var_to_np(loss))))\n",
    "        loss_rec[i_epoch, sets[setname]] = var_to_np(loss)\n",
    "    \n",
    "        predicted_labels = np.argmax(var_to_np(outputs), axis=1)\n",
    "        accuracy = np.mean(dataset.y  == predicted_labels)\n",
    "        print(\"{:6s} Accuracy: {:.1f}%\".format(\n",
    "            setname, accuracy * 100))\n",
    "        accuracy_rec[i_epoch, sets[setname]] = accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 383,
     "status": "ok",
     "timestamp": 1525488113804,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "UG0-7LY7EyiZ",
    "outputId": "6a969197-3ba3-429a-af05-a5329a14c0ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7928571428571428\n"
     ]
    }
   ],
   "source": [
    "print(max(accuracy_rec[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "k_KM9GGfsYeh"
   },
   "outputs": [],
   "source": [
    "def smoothing(record, smoothingRadius):\n",
    " if record.shape[0] > 2 * smoothingRadius :\n",
    "   record_smooth = np.zeros((record.shape[0] - (2 * smoothingRadius), record.shape[1]))\n",
    "   \n",
    "   for i in range(record_smooth.shape[0]):\n",
    "     for j in range(record_smooth.shape[1]):\n",
    "       record_smooth[i,j] = record[i:i+2*smoothingRadius,j].mean()\n",
    " \n",
    " return record_smooth\n",
    "\n",
    "\n",
    "# Define smoothing radius here\n",
    "smoothingRadius = 10\n",
    "\n",
    "population_loss_rec = loss_rec\n",
    "population_accuracy_rec = accuracy_rec\n",
    "population_loss_smooth = smoothing(loss_rec, smoothingRadius)\n",
    "population_accuracy_smooth = smoothing(accuracy_rec, smoothingRadius)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 327,
     "status": "ok",
     "timestamp": 1525488125118,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "MS7c6Q5ZqOtO",
    "outputId": "387d6e18-2218-41b2-dd5f-2e169b5930df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (dimshuffle): Expression(expression=_transpose_time_to_spat)\n",
      "  (conv_time): Conv2d (1, 10, kernel_size=(75, 1), stride=(1, 1))\n",
      "  (conv_spat): Conv2d (10, 5, kernel_size=(1, 15), stride=(1, 1), bias=False)\n",
      "  (bnorm): BatchNorm2d(5, eps=1e-05, momentum=0.1, affine=True)\n",
      "  (conv_nonlin): Expression(expression=square)\n",
      "  (pool): AvgPool2d(kernel_size=(60, 1), stride=(30, 1), padding=0, ceil_mode=False, count_include_pad=True)\n",
      "  (pool_nonlin): Expression(expression=safe_log)\n",
      "  (drop): Dropout(p=0.5)\n",
      "  (conv_classifier): Conv2d (5, 2, kernel_size=(116, 1), stride=(1, 1))\n",
      "  (softmax): LogSoftmax()\n",
      "  (squeeze): Expression(expression=_squeeze_final_output)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(model)\n",
    "\n",
    "# save/load only the model parameters(prefered solution)\n",
    "torch.save(model.state_dict(), \"myModel.pth\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "5yA2nYjFvMEs"
   },
   "outputs": [],
   "source": [
    "# load the saved network (makes it possible to run bottom form same starting point \n",
    "model.load_state_dict(torch.load(\"myModel.pth\"))\n",
    "\n",
    "#from google.colab import files\n",
    "#files.download('example.txt') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "FM26_bqltvx_"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from torch.nn import init\n",
    "\n",
    "for param in model.conv_classifier.parameters():\n",
    "    param.requires_grad = False\n",
    "    \n",
    "model.conv_classifier = nn.Conv2d(5, 2,(116, 1), bias=True).cuda()\n",
    "\n",
    "optimizer = optim.Adam(model.conv_classifier.parameters(),lr=0.00006)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 8551
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 408392,
     "status": "ok",
     "timestamp": 1525488551900,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "16OzXYG0At-M",
    "outputId": "a4e25a9f-3688-4e56-b728-753a5b0aadcf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:67: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Train  Loss: 0.70774\n",
      "Train  Accuracy: 53.0%\n",
      "Test   Loss: 0.71341\n",
      "Test   Accuracy: 51.8%\n",
      "Epoch 1\n",
      "Train  Loss: 0.67211\n",
      "Train  Accuracy: 58.9%\n",
      "Test   Loss: 0.68502\n",
      "Test   Accuracy: 53.9%\n",
      "Epoch 2\n",
      "Train  Loss: 0.63277\n",
      "Train  Accuracy: 65.3%\n",
      "Test   Loss: 0.66334\n",
      "Test   Accuracy: 58.6%\n",
      "Epoch 3\n",
      "Train  Loss: 0.60563\n",
      "Train  Accuracy: 68.0%\n",
      "Test   Loss: 0.64309\n",
      "Test   Accuracy: 58.9%\n",
      "Epoch 4\n",
      "Train  Loss: 0.60774\n",
      "Train  Accuracy: 65.3%\n",
      "Test   Loss: 0.64591\n",
      "Test   Accuracy: 60.4%\n",
      "Epoch 5\n",
      "Train  Loss: 0.56129\n",
      "Train  Accuracy: 72.1%\n",
      "Test   Loss: 0.61288\n",
      "Test   Accuracy: 63.9%\n",
      "Epoch 6\n",
      "Train  Loss: 0.54695\n",
      "Train  Accuracy: 73.0%\n",
      "Test   Loss: 0.60224\n",
      "Test   Accuracy: 67.9%\n",
      "Epoch 7\n",
      "Train  Loss: 0.52613\n",
      "Train  Accuracy: 75.2%\n",
      "Test   Loss: 0.58862\n",
      "Test   Accuracy: 67.9%\n",
      "Epoch 8\n",
      "Train  Loss: 0.57303\n",
      "Train  Accuracy: 66.4%\n",
      "Test   Loss: 0.62543\n",
      "Test   Accuracy: 62.5%\n",
      "Epoch 9\n",
      "Train  Loss: 0.55086\n",
      "Train  Accuracy: 69.4%\n",
      "Test   Loss: 0.60844\n",
      "Test   Accuracy: 66.1%\n",
      "Epoch 10\n",
      "Train  Loss: 0.48895\n",
      "Train  Accuracy: 78.3%\n",
      "Test   Loss: 0.56571\n",
      "Test   Accuracy: 70.0%\n",
      "Epoch 11\n",
      "Train  Loss: 0.69504\n",
      "Train  Accuracy: 57.5%\n",
      "Test   Loss: 0.72999\n",
      "Test   Accuracy: 58.9%\n",
      "Epoch 12\n",
      "Train  Loss: 0.69979\n",
      "Train  Accuracy: 57.9%\n",
      "Test   Loss: 0.73524\n",
      "Test   Accuracy: 58.2%\n",
      "Epoch 13\n",
      "Train  Loss: 0.47024\n",
      "Train  Accuracy: 79.6%\n",
      "Test   Loss: 0.55056\n",
      "Test   Accuracy: 72.9%\n",
      "Epoch 14\n",
      "Train  Loss: 0.58299\n",
      "Train  Accuracy: 63.8%\n",
      "Test   Loss: 0.64294\n",
      "Test   Accuracy: 62.1%\n",
      "Epoch 15\n",
      "Train  Loss: 0.44556\n",
      "Train  Accuracy: 81.8%\n",
      "Test   Loss: 0.53692\n",
      "Test   Accuracy: 69.6%\n",
      "Epoch 16\n",
      "Train  Loss: 0.44140\n",
      "Train  Accuracy: 82.1%\n",
      "Test   Loss: 0.53343\n",
      "Test   Accuracy: 72.1%\n",
      "Epoch 17\n",
      "Train  Loss: 0.47309\n",
      "Train  Accuracy: 77.4%\n",
      "Test   Loss: 0.55376\n",
      "Test   Accuracy: 71.8%\n",
      "Epoch 18\n",
      "Train  Loss: 0.64433\n",
      "Train  Accuracy: 60.8%\n",
      "Test   Loss: 0.70048\n",
      "Test   Accuracy: 61.1%\n",
      "Epoch 19\n",
      "Train  Loss: 0.42611\n",
      "Train  Accuracy: 82.7%\n",
      "Test   Loss: 0.52284\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 20\n",
      "Train  Loss: 0.44798\n",
      "Train  Accuracy: 79.3%\n",
      "Test   Loss: 0.53567\n",
      "Test   Accuracy: 73.6%\n",
      "Epoch 21\n",
      "Train  Loss: 0.76214\n",
      "Train  Accuracy: 58.2%\n",
      "Test   Loss: 0.80196\n",
      "Test   Accuracy: 58.6%\n",
      "Epoch 22\n",
      "Train  Loss: 0.78373\n",
      "Train  Accuracy: 57.4%\n",
      "Test   Loss: 0.82476\n",
      "Test   Accuracy: 57.9%\n",
      "Epoch 23\n",
      "Train  Loss: 0.40514\n",
      "Train  Accuracy: 84.2%\n",
      "Test   Loss: 0.51015\n",
      "Test   Accuracy: 74.6%\n",
      "Epoch 24\n",
      "Train  Loss: 0.39857\n",
      "Train  Accuracy: 84.6%\n",
      "Test   Loss: 0.50903\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 25\n",
      "Train  Loss: 0.46836\n",
      "Train  Accuracy: 75.6%\n",
      "Test   Loss: 0.55478\n",
      "Test   Accuracy: 72.1%\n",
      "Epoch 26\n",
      "Train  Loss: 0.54129\n",
      "Train  Accuracy: 67.8%\n",
      "Test   Loss: 0.61692\n",
      "Test   Accuracy: 66.4%\n",
      "Epoch 27\n",
      "Train  Loss: 0.41047\n",
      "Train  Accuracy: 81.8%\n",
      "Test   Loss: 0.51079\n",
      "Test   Accuracy: 75.4%\n",
      "Epoch 28\n",
      "Train  Loss: 0.38764\n",
      "Train  Accuracy: 85.5%\n",
      "Test   Loss: 0.50056\n",
      "Test   Accuracy: 75.7%\n",
      "Epoch 29\n",
      "Train  Loss: 0.38728\n",
      "Train  Accuracy: 84.7%\n",
      "Test   Loss: 0.49917\n",
      "Test   Accuracy: 76.1%\n",
      "Epoch 30\n",
      "Train  Loss: 0.55203\n",
      "Train  Accuracy: 67.1%\n",
      "Test   Loss: 0.62791\n",
      "Test   Accuracy: 66.1%\n",
      "Epoch 31\n",
      "Train  Loss: 0.63292\n",
      "Train  Accuracy: 62.3%\n",
      "Test   Loss: 0.69849\n",
      "Test   Accuracy: 63.9%\n",
      "Epoch 32\n",
      "Train  Loss: 0.40382\n",
      "Train  Accuracy: 82.5%\n",
      "Test   Loss: 0.50830\n",
      "Test   Accuracy: 74.3%\n",
      "Epoch 33\n",
      "Train  Loss: 0.41273\n",
      "Train  Accuracy: 81.0%\n",
      "Test   Loss: 0.51344\n",
      "Test   Accuracy: 74.6%\n",
      "Epoch 34\n",
      "Train  Loss: 0.38900\n",
      "Train  Accuracy: 84.7%\n",
      "Test   Loss: 0.49968\n",
      "Test   Accuracy: 75.7%\n",
      "Epoch 35\n",
      "Train  Loss: 0.40794\n",
      "Train  Accuracy: 81.6%\n",
      "Test   Loss: 0.51011\n",
      "Test   Accuracy: 75.0%\n",
      "Epoch 36\n",
      "Train  Loss: 0.38381\n",
      "Train  Accuracy: 85.0%\n",
      "Test   Loss: 0.49753\n",
      "Test   Accuracy: 76.1%\n",
      "Epoch 37\n",
      "Train  Loss: 0.80647\n",
      "Train  Accuracy: 57.6%\n",
      "Test   Loss: 0.85098\n",
      "Test   Accuracy: 57.9%\n",
      "Epoch 38\n",
      "Train  Loss: 0.38152\n",
      "Train  Accuracy: 85.4%\n",
      "Test   Loss: 0.49558\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 39\n",
      "Train  Loss: 0.63762\n",
      "Train  Accuracy: 62.1%\n",
      "Test   Loss: 0.70347\n",
      "Test   Accuracy: 63.9%\n",
      "Epoch 40\n",
      "Train  Loss: 0.38605\n",
      "Train  Accuracy: 84.8%\n",
      "Test   Loss: 0.49769\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 41\n",
      "Train  Loss: 0.45433\n",
      "Train  Accuracy: 76.9%\n",
      "Test   Loss: 0.54571\n",
      "Test   Accuracy: 71.8%\n",
      "Epoch 42\n",
      "Train  Loss: 0.37763\n",
      "Train  Accuracy: 85.7%\n",
      "Test   Loss: 0.49807\n",
      "Test   Accuracy: 76.1%\n",
      "Epoch 43\n",
      "Train  Loss: 0.54185\n",
      "Train  Accuracy: 68.4%\n",
      "Test   Loss: 0.61921\n",
      "Test   Accuracy: 66.1%\n",
      "Epoch 44\n",
      "Train  Loss: 0.38176\n",
      "Train  Accuracy: 85.1%\n",
      "Test   Loss: 0.49658\n",
      "Test   Accuracy: 75.7%\n",
      "Epoch 45\n",
      "Train  Loss: 0.40775\n",
      "Train  Accuracy: 81.8%\n",
      "Test   Loss: 0.51050\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 46\n",
      "Train  Loss: 0.37737\n",
      "Train  Accuracy: 85.9%\n",
      "Test   Loss: 0.49589\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 47\n",
      "Train  Loss: 0.49310\n",
      "Train  Accuracy: 72.1%\n",
      "Test   Loss: 0.57810\n",
      "Test   Accuracy: 68.9%\n",
      "Epoch 48\n",
      "Train  Loss: 0.49288\n",
      "Train  Accuracy: 72.3%\n",
      "Test   Loss: 0.57824\n",
      "Test   Accuracy: 68.9%\n",
      "Epoch 49\n",
      "Train  Loss: 0.47247\n",
      "Train  Accuracy: 74.6%\n",
      "Test   Loss: 0.56114\n",
      "Test   Accuracy: 70.7%\n",
      "Epoch 50\n",
      "Train  Loss: 0.41636\n",
      "Train  Accuracy: 81.3%\n",
      "Test   Loss: 0.51692\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 51\n",
      "Train  Loss: 0.37674\n",
      "Train  Accuracy: 85.8%\n",
      "Test   Loss: 0.49539\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 52\n",
      "Train  Loss: 0.78148\n",
      "Train  Accuracy: 58.9%\n",
      "Test   Loss: 0.82720\n",
      "Test   Accuracy: 60.4%\n",
      "Epoch 53\n",
      "Train  Loss: 0.37504\n",
      "Train  Accuracy: 85.9%\n",
      "Test   Loss: 0.49425\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 54\n",
      "Train  Loss: 0.50317\n",
      "Train  Accuracy: 71.3%\n",
      "Test   Loss: 0.58729\n",
      "Test   Accuracy: 68.6%\n",
      "Epoch 55\n",
      "Train  Loss: 0.70206\n",
      "Train  Accuracy: 60.2%\n",
      "Test   Loss: 0.76172\n",
      "Test   Accuracy: 61.8%\n",
      "Epoch 56\n",
      "Train  Loss: 0.37398\n",
      "Train  Accuracy: 86.0%\n",
      "Test   Loss: 0.49514\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 57\n",
      "Train  Loss: 0.81868\n",
      "Train  Accuracy: 57.8%\n",
      "Test   Loss: 0.86255\n",
      "Test   Accuracy: 58.6%\n",
      "Epoch 58\n",
      "Train  Loss: 0.87280\n",
      "Train  Accuracy: 56.7%\n",
      "Test   Loss: 0.91185\n",
      "Test   Accuracy: 56.8%\n",
      "Epoch 59\n",
      "Train  Loss: 0.46351\n",
      "Train  Accuracy: 75.3%\n",
      "Test   Loss: 0.55445\n",
      "Test   Accuracy: 70.7%\n",
      "Epoch 60\n",
      "Train  Loss: 0.38987\n",
      "Train  Accuracy: 83.8%\n",
      "Test   Loss: 0.49883\n",
      "Test   Accuracy: 75.4%\n",
      "Epoch 61\n",
      "Train  Loss: 0.37888\n",
      "Train  Accuracy: 85.2%\n",
      "Test   Loss: 0.49518\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 62\n",
      "Train  Loss: 0.40491\n",
      "Train  Accuracy: 81.4%\n",
      "Test   Loss: 0.50861\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 63\n",
      "Train  Loss: 0.81084\n",
      "Train  Accuracy: 58.1%\n",
      "Test   Loss: 0.85471\n",
      "Test   Accuracy: 58.6%\n",
      "Epoch 64\n",
      "Train  Loss: 0.41004\n",
      "Train  Accuracy: 80.8%\n",
      "Test   Loss: 0.51239\n",
      "Test   Accuracy: 75.0%\n",
      "Epoch 65\n",
      "Train  Loss: 0.39265\n",
      "Train  Accuracy: 83.3%\n",
      "Test   Loss: 0.50128\n",
      "Test   Accuracy: 75.7%\n",
      "Epoch 66\n",
      "Train  Loss: 0.56240\n",
      "Train  Accuracy: 66.7%\n",
      "Test   Loss: 0.63956\n",
      "Test   Accuracy: 64.6%\n",
      "Epoch 67\n",
      "Train  Loss: 0.38213\n",
      "Train  Accuracy: 84.8%\n",
      "Test   Loss: 0.49620\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 68\n",
      "Train  Loss: 0.52059\n",
      "Train  Accuracy: 70.1%\n",
      "Test   Loss: 0.60251\n",
      "Test   Accuracy: 67.9%\n",
      "Epoch 69\n",
      "Train  Loss: 0.60588\n",
      "Train  Accuracy: 64.0%\n",
      "Test   Loss: 0.67730\n",
      "Test   Accuracy: 64.3%\n",
      "Epoch 70\n",
      "Train  Loss: 0.38580\n",
      "Train  Accuracy: 84.1%\n",
      "Test   Loss: 0.49633\n",
      "Test   Accuracy: 76.1%\n",
      "Epoch 71\n",
      "Train  Loss: 0.79989\n",
      "Train  Accuracy: 58.2%\n",
      "Test   Loss: 0.84580\n",
      "Test   Accuracy: 58.9%\n",
      "Epoch 72\n",
      "Train  Loss: 0.45699\n",
      "Train  Accuracy: 76.5%\n",
      "Test   Loss: 0.54905\n",
      "Test   Accuracy: 71.1%\n",
      "Epoch 73\n",
      "Train  Loss: 0.37567\n",
      "Train  Accuracy: 85.8%\n",
      "Test   Loss: 0.49359\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 74\n",
      "Train  Loss: 0.42641\n",
      "Train  Accuracy: 79.6%\n",
      "Test   Loss: 0.52489\n",
      "Test   Accuracy: 74.3%\n",
      "Epoch 75\n",
      "Train  Loss: 0.85212\n",
      "Train  Accuracy: 57.4%\n",
      "Test   Loss: 0.89146\n",
      "Test   Accuracy: 57.5%\n",
      "Epoch 76\n",
      "Train  Loss: 0.42941\n",
      "Train  Accuracy: 79.1%\n",
      "Test   Loss: 0.52704\n",
      "Test   Accuracy: 73.9%\n",
      "Epoch 77\n",
      "Train  Loss: 0.37493\n",
      "Train  Accuracy: 85.9%\n",
      "Test   Loss: 0.49464\n",
      "Test   Accuracy: 77.1%\n",
      "Epoch 78\n",
      "Train  Loss: 0.43236\n",
      "Train  Accuracy: 78.9%\n",
      "Test   Loss: 0.52938\n",
      "Test   Accuracy: 73.6%\n",
      "Epoch 79\n",
      "Train  Loss: 0.37444\n",
      "Train  Accuracy: 86.0%\n",
      "Test   Loss: 0.49392\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 80\n",
      "Train  Loss: 0.37572\n",
      "Train  Accuracy: 85.5%\n",
      "Test   Loss: 0.49281\n",
      "Test   Accuracy: 75.7%\n",
      "Epoch 81\n",
      "Train  Loss: 0.38036\n",
      "Train  Accuracy: 85.0%\n",
      "Test   Loss: 0.49454\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 82\n",
      "Train  Loss: 0.40997\n",
      "Train  Accuracy: 81.3%\n",
      "Test   Loss: 0.51248\n",
      "Test   Accuracy: 74.3%\n",
      "Epoch 83\n",
      "Train  Loss: 0.37304\n",
      "Train  Accuracy: 86.2%\n",
      "Test   Loss: 0.49336\n",
      "Test   Accuracy: 77.1%\n",
      "Epoch 84\n",
      "Train  Loss: 0.62579\n",
      "Train  Accuracy: 63.1%\n",
      "Test   Loss: 0.69483\n",
      "Test   Accuracy: 63.9%\n",
      "Epoch 85\n",
      "Train  Loss: 0.52259\n",
      "Train  Accuracy: 69.8%\n",
      "Test   Loss: 0.60461\n",
      "Test   Accuracy: 67.9%\n",
      "Epoch 86\n",
      "Train  Loss: 0.37452\n",
      "Train  Accuracy: 86.0%\n",
      "Test   Loss: 0.49331\n",
      "Test   Accuracy: 77.1%\n",
      "Epoch 87\n",
      "Train  Loss: 0.37616\n",
      "Train  Accuracy: 85.7%\n",
      "Test   Loss: 0.49577\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 88\n",
      "Train  Loss: 0.40070\n",
      "Train  Accuracy: 82.1%\n",
      "Test   Loss: 0.50591\n",
      "Test   Accuracy: 74.6%\n",
      "Epoch 89\n",
      "Train  Loss: 0.37476\n",
      "Train  Accuracy: 85.4%\n",
      "Test   Loss: 0.49198\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 90\n",
      "Train  Loss: 0.38272\n",
      "Train  Accuracy: 84.6%\n",
      "Test   Loss: 0.49623\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 91\n",
      "Train  Loss: 0.38158\n",
      "Train  Accuracy: 84.6%\n",
      "Test   Loss: 0.49551\n",
      "Test   Accuracy: 76.8%\n",
      "Epoch 92\n",
      "Train  Loss: 0.62965\n",
      "Train  Accuracy: 63.1%\n",
      "Test   Loss: 0.69682\n",
      "Test   Accuracy: 64.3%\n",
      "Epoch 93\n",
      "Train  Loss: 0.37699\n",
      "Train  Accuracy: 85.1%\n",
      "Test   Loss: 0.49359\n",
      "Test   Accuracy: 76.4%\n",
      "Epoch 94\n",
      "Train  Loss: 0.38578\n",
      "Train  Accuracy: 84.1%\n",
      "Test   Loss: 0.49656\n",
      "Test   Accuracy: 76.1%\n",
      "Epoch 95\n",
      "Train  Loss: 0.37667\n",
      "Train  Accuracy: 85.8%\n",
      "Test   Loss: 0.49505\n",
      "Test   Accuracy: 76.4%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 96\n",
      "Train  Loss: 0.88574\n",
      "Train  Accuracy: 56.8%\n",
      "Test   Loss: 0.92035\n",
      "Test   Accuracy: 56.8%\n",
      "Epoch 97\n",
      "Train  Loss: 0.67782\n",
      "Train  Accuracy: 61.3%\n",
      "Test   Loss: 0.74026\n",
      "Test   Accuracy: 63.2%\n",
      "Epoch 98\n",
      "Train  Loss: 0.55392\n",
      "Train  Accuracy: 67.9%\n",
      "Test   Loss: 0.63096\n",
      "Test   Accuracy: 66.1%\n",
      "Epoch 99\n",
      "Train  Loss: 0.64207\n",
      "Train  Accuracy: 62.5%\n",
      "Test   Loss: 0.70865\n",
      "Test   Accuracy: 63.9%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from braindecode.torch_ext.util import np_to_var, var_to_np\n",
    "from braindecode.datautil.iterators import get_balanced_batches\n",
    "import torch.nn.functional as F\n",
    "from numpy.random import RandomState\n",
    "rng = RandomState(None)\n",
    "#rng = RandomState((2017,6,30))\n",
    "\n",
    "nb_epoch = 100\n",
    "loss_rec = np.zeros((nb_epoch,2))\n",
    "accuracy_rec = np.zeros((nb_epoch,2))\n",
    "\n",
    "\n",
    "\n",
    "def adjust_learning_rate(optimizer, epoch):\n",
    "    \"\"\"Sets the learning rate to the initial LR decayed by 10% every 30 epochs\"\"\"\n",
    "    lr = 0.00006 * (0.1 ** (epoch // 30))\n",
    "    for param_group in optimizer.param_groups:\n",
    "        param_group['lr'] = lr\n",
    "\n",
    "\n",
    "for i_epoch in range(nb_epoch):\n",
    "    i_trials_in_batch = get_balanced_batches(len(train_set.X), rng, shuffle=True,\n",
    "                                            batch_size=32)\n",
    "    \n",
    "    \n",
    "    adjust_learning_rate(optimizer,i_epoch)\n",
    "    \n",
    "    \n",
    "    # Set model to training mode\n",
    "    model.train()\n",
    "    \n",
    "    \n",
    "    for i_trials in i_trials_in_batch:\n",
    "        # Have to add empty fourth dimension to X\n",
    "        batch_X = train_set.X[i_trials][:,:,:,None]\n",
    "        batch_y = train_set.y[i_trials]\n",
    "        net_in = np_to_var(batch_X)\n",
    "        if cuda:\n",
    "            net_in = net_in.cuda()\n",
    "        net_target = np_to_var(batch_y)\n",
    "        if cuda:\n",
    "            net_target = net_target.cuda()\n",
    "        # Remove gradients of last backward pass from all parameters\n",
    "        optimizer.zero_grad()\n",
    "        # Compute outputs of the network\n",
    "        outputs = model(net_in)\n",
    "        # Compute the loss\n",
    "        loss = F.nll_loss(outputs, net_target)\n",
    "        # Do the backpropagation\n",
    "        loss.backward()\n",
    "        # Update parameters with the optimizer\n",
    "        optimizer.step()\n",
    "\n",
    "    # Print some statistics each epoch\n",
    "    model.eval()\n",
    "    print(\"Epoch {:d}\".format(i_epoch))\n",
    "    \n",
    "    sets = {'Train' : 0, 'Test' : 1}\n",
    "    for setname, dataset in (('Train', train_set), ('Test', test_set)):\n",
    "        i_trials_in_batch = get_balanced_batches(len(dataset.X), rng, batch_size=32, shuffle=False)\n",
    "        outputs = []\n",
    "        net_targets = []\n",
    "        for i_trials in i_trials_in_batch:\n",
    "            batch_X = dataset.X[i_trials][:,:,:,None]\n",
    "            batch_y = dataset.y[i_trials]\n",
    "            \n",
    "            net_in = np_to_var(batch_X)\n",
    "            if cuda:\n",
    "                net_in = net_in.cuda()\n",
    "            net_target = np_to_var(batch_y)\n",
    "            if cuda:\n",
    "                net_target = net_target.cuda()\n",
    "            net_target = var_to_np(net_target)\n",
    "            output = var_to_np(model(net_in))\n",
    "            outputs.append(output)\n",
    "            net_targets.append(net_target)\n",
    "        net_targets = np_to_var(np.concatenate(net_targets))\n",
    "        outputs = np_to_var(np.concatenate(outputs))\n",
    "        loss = F.nll_loss(outputs, net_targets)\n",
    " \n",
    "        print(\"{:6s} Loss: {:.5f}\".format(\n",
    "            setname, float(var_to_np(loss))))\n",
    "        loss_rec[i_epoch, sets[setname]] = var_to_np(loss)\n",
    "    \n",
    "        predicted_labels = np.argmax(var_to_np(outputs), axis=1)\n",
    "        accuracy = np.mean(dataset.y  == predicted_labels)\n",
    "        print(\"{:6s} Accuracy: {:.1f}%\".format(\n",
    "            setname, accuracy * 100))\n",
    "        accuracy_rec[i_epoch, sets[setname]] = accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 977
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2183,
     "status": "ok",
     "timestamp": 1525488558096,
     "user": {
      "displayName": "TheAtom2626",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
      "userId": "114038612102964610367"
     },
     "user_tz": 240
    },
    "id": "ZxsqB7uLAgEv",
    "outputId": "fcc2b9a7-b185-4858-e5d4-797ba65a2e47"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAHgCAYAAAC1ouv3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFQdSgXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe07189d1d0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAHgCAYAAACFLvrWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd8HPWZ/98zW9R7tWSrWO6yjW0M\nxphiHNu0QCgJmAslAVIu4VLJXc73y3EpkOQu5BJyqVzIEUJxSEwLAdNsmg3GvVuWZDVLVm+rtrsz\n8/tjdrZYbVfaptX3/XrxYsuUZ3ZW688883meR9I0TUMgEAgEAoFAIBBMCDnSAQgEAoFAIBAIBFMZ\nIagFAoFAIBAIBIJJIAS1QCAQCAQCgUAwCYSgFggEAoFAIBAIJoEQ1AKBQCAQCAQCwSQQglogEAgE\nAoFAIJgE5kgHMFlaW3sntF5GRiKdnf1BjiZ6iOXji+VjA3F8U51Aji8nJyXE0UQfsfCbLWIZGRHL\nyIhYRmYqxjLWb/a0zVCbzaZIhxBSYvn4YvnYQBzfVCfWjy9SRNPnKmIZGRHLyIhYRibWYpm2glog\nEAgEAoFAIAgGQlALBAKBQCAQCASTQAhqgUAgEAgEAoFgEghBLRAIBAKBQCAQTAIhqAUCgUAgEAgE\ngkkgBLVAIBAIBAKBQDAJhKAWCASCMfj9kT/x1Im/RDoMgUAQYzx5/C88duTJSIchCBJCUIeIHTve\n9Gu5n//8YRobz4Q4GoFAMFFOdlZyouNUpMMQCAQxxJBi54Oze9jbcpCOwc5IhzOliRa9JQR1CGhq\nauSNN7b5texXv/pNCgoKQxyRIBoZcA6iqEqkwxCMg6KqDCn2SIch8AOH6qSisxJN0yIdikAwJjXd\ndaiaCsCRtuMRjmbqEk16a8qPHo9GfvrTH3P8+FEuvfQCNm68mqamRn72s1/xwx9+j9bWFgYGBrj7\n7s+zZs2l3Hff5/nGN/6Z7dvfpK/PRl1dLWfONPCVr3yT1avXRPpQBCGiz9HPA7t+zOUzL+a62VdG\nOhzBGCiagqI4Ix2GwA9eqPw72xve49Z5N3DZzIsjHY5AMCqV3afdjw+3Hxff1wkSLL11/fVXTTqW\nsAvqhx56iIMHDyJJEps3b2bp0qXu99544w1+/etfY7Vaufbaa7n99tsnvb8/v1XJRydahr1uMkko\nysSyGBcsyOWWdXNGff+22+5g69Y/U1paRl1dDb/61f/S2dnBhRdexNVXf5wzZxr4zne+zZo1l/qs\n19LSzE9+8ggffLCTF174qxDUMUx97xkGnAO09rdFOhTBOCiagqqpKKqCSY6eUbkCXzoHu3j3zC4A\nXqh6laU55aTHpUU4KoFgZKq7agDIis+gorOKIcVOnMka2aAmyWh6azT80WHh0ltTTlDv3r2b2tpa\ntmzZQlVVFZs3b2bLli0AqKrK97//fZ577jnS09P53Oc+x/r168nPzw9niEFn4cJyAFJSUjl+/Cgv\nvrgVSZLp6eketuzSpcsAyM3NxWazhTVOQXhp7DsLgFMTlo9oRtM0923ZIcVOopwQ4YgEo/FKzZs4\nNYWFmfM43lHBX069xL2LJ5+UEQiCjaIqVPfUkp+Ux3nZ5WyrfYsTHac4L6c80qFNaSKtt8IqqHft\n2sX69esBKCsro7u7G5vNRnJyMp2dnaSmppKZmQnARRddxM6dO7npppsmtc9b1s0Z8eomJyeF1tbe\nSW3bHywWCwCvv/4qPT09/PKX/0tPTw/33nvHsGVNJk/2S3gAY5smmy6oFVVYCaIZxeuCZ0gZItEi\nBHUkUFQFVVVHfb+1v51dTR+Rl5jDPy79LD/b/xv2txziSNtxFmcvDGOkAsH4NNgasSt25qSVsCR7\nIdtq3+JI27EpL6hH01ujEWwdFmm9FdaixLa2NjIyMtzPMzMzaW1tdT/u6+ujpqYGh8PBhx9+SFvb\n1LwdLssyiuKbeezq6mLGjAJkWebtt9/C4XBEKDpBNNDY1wyAUxQlRjWK5hFxojAxcvzoo5/zk52/\nG/X9V2reQNVUri3dgEk2cdv8m5ElmWdOPifOmyDqqOrS/dNl6aUUp84i2ZLEkfYT7rthgdA91MuB\nlsM4p2lyJpr0VkSLEr2vCiRJ4kc/+hGbN28mJSWFmTNn+rWNjIxEzOaJ+RpzclImtN54nH/+Eh58\nsILZs0tITo4nJyeFm266jn/8x3/k1Knj3HzzzRQUzGDLlsexWs1kZCSRlBTnXrazMwmr1Tzp+EJ1\nfNHAVD42VVNp6tcFtWwe+Vim8vH5w1Q5vj57v/txYqqZnEz/4p4qxzdVSLWmsOfMQdbNuIzi1Fk+\n753ta2H32X0UJOWzPFevySlIzmd90eW8Vrudl6tf46a5H49E2ALBiFR21wBQllaKLMkszlrIB2f3\nUN97Ztj3eyQUVeFo+wl2Nn3EUZcQ/9S8T7B25vSruyouLuXkyRPMmFFAeno6AGvXruPb3/4Gx44d\n4dprryc3N5c//OHRkMcSVkGdm5vrk3VuaWkhJyfH/fzCCy/kqaeeAuDhhx+msHD89iadnf3jLjMS\nobV8WHj22Zfcz1pbe4mLS+Oxx55yv7Z69RUA3HrrXQBs2vQZ97IZGTP46U9/Nan4wmVpiQRT/dja\nBjoYcg4BMDBkH3YsU+H4OgY7qeqq4YL85QGvOxWOz6DX7vHWnW3rJFXJHHedQI5PCG//2FC8lhOd\np3i9dgf3LvG9ffvy6dfQ0Pj47I3Ikuem69Ul69nXfJDtDe+xasb5FCbPCHfYAsEwNE2jqus0GXHp\nZCXod+wXZ+uC+nDb8XEFtaIq/Hz/b6lyifKCpHwa+85S2XV6WgrqjIwMtm592ee1GTMKePzxZ9zP\nN268GoDPfvZzAMye7bGlzJ49h//5n9HvfgVCWC0fa9asYds2vV/g0aNHyc3NJTk52f3+vffeS3t7\nO/39/Wzfvp3Vq1eHMzyBICw0uQoSARRtat6me6Pubf7v2NO0DXREOpSQcq6HWhAZ5mfMYXZGEQda\nj9Dc3+p+/XDbMfa1HKIoZSZLs339p1aThU/N+wSqprLl5HOiLkUQFTT3t2Jz9FGWXuJ+bWHmXEyS\niSNtx8Zdf0fD+1R117Awcx7fvuBrbL7w66RaU6juqhHf8QgTVkG9YsUKysvL2bRpEz/4wQ944IEH\n2Lp1K6+//joAt9xyC3fffTf/8A//wOc//3l3gaJAEEs02jyCeqp6qAddGfZYF5mKKjzU0YAkSdyw\n8Eo0NN6ofRvQvaN/Ov4sZtnM7Qs/hSRJw9ZbnL2Q87LLqequ4cOze8MdtkAwjCpX/+k56aXu1+LN\n8cxNn029rZHDbceo7DpNVVeNzx0ygLaBdl6q3kayJYnPLLqNWSkFSJJEaVox3fYeOoe6wnosAl/C\n7qG+//77fZ4vWLDA/Xjjxo1s3Lgx3CEJBGHFaJknIflkQKcSRtyxPulRZKijhwsLl5GbkM3us3u5\ndvYGnjz+F2yOPj459/ox7Rw3z72e4x0VPFf5MkuzF5FoSQxj1AKBL1Wu/tNlaaU+ry/JWcSJzlP8\n5tD/uV+LM1n59IJPcn7eMjRN4+kTW3GoDj694JMkW5Pcy81OK+Zg6xGqu2vJjM9AEBnEpESBIMw0\n2s5iNVlJNCdM2cpsI7Me6320fQW1yFBHElmWWV90OU+d/Cu/2P8oZ/tbWJQ5f1zfaFZCBleXrOeF\n6ld4sXobm+bfGKaIBQLcQ6EMKrtOk2hOID8p12e5i2dcgENxMKTY0dCwK3beb/yQx44+RUVXNbOS\nCzjReYpFWfNZmbfMZ93ZacUAVHfXDntPED6EoBYIwoiiKjT3tzIzpYB+Rz92ZWq2TzS837HeR9v7\nH8IhpxDUkebCGefz8unXONvfQrIlidsX3jKi1eNc1hVdygdn9/LemQ8oSMrj0sLVfq0nEEwGh+Lg\nv/f9hqb+s5SkFlOSOov2wQ6WZC/0KaAFsJqsbChe6/PaJQWr+P3RJ3nvzAfuZTbNu2nYd3dWciFm\nycRpV6GiIDKE1UMtEEx3WgbaUDSFwqR8VFUSGeooR1g+oguLbGZjyTpkSeb2hZ8iLc6/Lilm2cwd\nCz9FgjmeLRXP8+tDf6B7yNOJxak66XcMhCpswTTlhepXqO2tJ8GSQEVnJa/VbgeG2z1GIy8pl/vP\nv481BasAuLHsWndnEG8sJguzUmbSYGsSd9IiiMhQh4gdO95k7dqP+b38gQP7KC4uISNDFGLGMo22\nJgDSzdm0dp7AkjBFM9TqdPFQe4oSB5yDEYxEYHB54cWsyj+fBHN8QOuVphXzb6u+wRPH/szR9hM8\ntPunzM+YQ1NfM2f7W9A0jU+UXT0sSygQnIvN0ce7Dbu4aMZKMuLTR1zmaPtJtte/R15iLv919Waa\nmjup7D5Nc18LlxRe5Pe+rCYL/7DgZm6ccw0J5tEntc5OK+Z0Ty11PfXMzSgL+JimMtGit0SGOgQ0\nNTXyxhvbAlrn5ZdfpLMztluQCTwTEmV7KqgyKoFPxooGjMx0zGeove4gDDhEhjoakCQpYDFtkB6X\nxpeX3cOn5n6CIWWIvS0HaR/soDhlJqnWZJ6v+jsvVr0q2o8JxuQvFS/xt9Ov8d/7fk37CK1De+02\nnji+BZNk4rPltxFvjiPZmsSynMVcWbJuQt/fscQ06BeMoPuopxPRpLdEhjoE/PSnP+b48aM89tjv\nqK6upLe3F0VR+NrXvsWcOXP505/+j7ff3o4sy6xZcykLFy7i3Xd3cPp0NT/4wX+Sn58f6UMQhIgm\nV8s8hy0JTZPRpqig9mSop6ZlxV+cXm3zjFaBgqmNLMmsnbWGlXnLGFSGyIxPR5Zk2gc6+MWBR9lW\n+xYDzgE+Ne8Tw3yuAkF1dy0fNe8jyZJI+2An/73vN3xtxRfITsgC9CLEJ47/mV67jRvnXMuslPEH\n1AWD0rQid3zTiWjSWzEvqLdW/o39LYeHvW6SJRR1YlmI5blLuGnO6KNsb7vtDrZu/TOyLLNq1cVc\nd90NnD5dzc9//hN+9rNf8cwzf+L551/FZDLx/PN/5YILLmLOnHl84xv/LMR0jNPU10ySJZHOTg1U\nCSQNVVOn3D/cTldR4lTto+0vDq+i0QEhqGOKZGsSyXhaj2UlZPL1FV/ilwf/l3fO7ELVVG5bcHME\nIxREG6qm8peKFwH4/JK7qOo6zYvVr/Lf+37DtaUbqeyq5mj7CWyOPhZkzGXdrEvDFlt6XBpZ8Rmc\n7q5F07SIFN2OprdGwx8dNpX0VswL6khy+PAhuro62bbt7wAMDekezLVrP8bXvvYlNmy4io0br4pk\niIIw0+fsJ82aytm6AUjQRbRTVbCappagdmeoY9zy4VBEUeJ0Ii0uha8t/wI/2/9b3mv8kMXZC1mS\nvSjSYQmihN1n91HbW8/KvGXMSS9lTnopsiTzfNXfefLEswCkWVNYU3Ah182+KuyJktK0YvY0H6Cl\nv5W8c9ryxTrRoLdiXlDfNOfjI17d5OSk0NraO8IawcNiMfP1r3+LxYuX+rx+//3/Sm1tDW+99Tr/\n9E9f4He/ezykcQiiB0VVMMkmmtr7oFD/sdVb0Fn83sbR9hPMTC70u8NBKHB7qGM9Q+1laRHV89OD\nREsin1l0Gz/+6Oc8feKvlK0qEcNgBAw6B3mh6hUssoUbyq5xv76heC1pcal0DHayKGs+M5MLInbH\ncXZaCXuaD1DdXRsRQT2a3hqNYOqwaNBbUystNkWQZRlFUVi0aDHvvLMDgNOnq3nmmT9hs9n4wx8e\npbi4hM9+9nOkpKTR39/nXme6Y1ccDDhjt32VU1NAk+npd4Cm35ILRJR2DXXzq4OP8WrNm6EK0S/s\nTt0KMVXb/vmL0+tv0i4E9bShIDmfq0s30G3v5S+nXop0OIIw0TnYxfuNH7Ll5PM8vPeX/Mu73+V7\nH/yEX+x/lF8efIweey8bi9cO6+xxYf4Krir5GEUpMyNq3zN81Kd7po+POpr0VsxnqCNBcXEpJ0+e\nYMaMApqbz/KlL92Lqqp87Wv3k5ycTFdXJ5/73J0kJCSyePFSUlPTWLZsBf/v//0LP/zhw8yePb1a\n3njz6JE/0tbfzgOr/znSoQQdTdNQVAVDP2uqYfnwX5Qardsi3cJtwO4ACbr7Y7uVnMPrYseuxr6g\nfuihhzh48CCSJLF582aWLvVke5qamvjGN76Bw+Fg0aJFfO973xt3nanMhqLLOdh6mA/P7mV57hJh\n/YhxVE3Vu3YMdgJ68WpmfAY2h43m/hYAsuIzWV90eSTDHJPCpBlYZQtV06gwMZr0lhDUISAjI4Ot\nW18e9f2vf324WLz77s9z992fD2VYU4LOwS46h7oiHUZIUDUVDQ2noZ81w/Lh/5WyIb6NosBgUtfb\nQK/dRnnWgnGXVdFjtjtjPEPtJaidWmwL6t27d1NbW8uWLVuoqqpi8+bNbNmyxf3+j370I+6++242\nbNjAd7/7XRobG2loaBhznamMSTZxx8Jb3daP2atKSBLWj6hE0zSa+pqZkZQ34WK8qq4a2gc7WZy1\ngGtKN1CQlI/FpFvx7IqdrqFuki3JWE3WYIYeVEyyieLUWVR2nWbAOTBuq71YIJr0lrB8CKIKRVVi\n1pdrCGeH0ThC8xQl+oszhANV/lLxEr8/8ie/ljXa/cW+5cNzfE7NiapNzTaH/rBr1y7Wr18PQFlZ\nGd3d3dhsNgBUVWXv3r2sW7cOgAceeICCgoIx14kFvK0fTxzfIvpTRyn7Wg7x4O6f8nbDzglv46Pm\n/QCsm3UZxamz3GIa9JHfuYk5JFqiX6CWphWjoVHb0xDpUKYdQlALogqnpqChxaRwMcSw3a7/oywR\neIZacRcDBl/IDilDDCl2vz57Q1A7YvTix+Dci51YLkxsa2sjI8Mz1jgzM5PW1lYAOjo6SEpK4oc/\n/CG33XYbDz/88LjrxAobi9cyP2MOh9uO82b9Oz7vNfQ28mLVqxxtPxmTv1lThcquagBePv0a/Y7+\ngNd3qk72txwizZrC3IzZwQ4vrJSkunzU3XURjmT6ISwfgqjCGBQyFVvJjYchhgeHNJITLGAyoxCY\nOPZYPoIvZL07d4z12WuahiZNkwy1S1BrqowkqwwpQxOe0jfV8M7GappGc3Mzd955J4WFhXz+859n\nx44dY64zGhkZiZjNpgnFlJMTmc4237zsc/zLtod4oeoVlhctIFtLZn/3fh7f/6zeCaYWMhPSuaxk\nFVfPvYKMhLSwxhepz2UkIhFL44EmAPqdA7zd/C53Lv9kQLHsOXOQfucAH5/3MfJyQ3PuwvW5rExe\nyO8OQ+NQ46j7nO7fl9GYbCxCUAuiCkPUBdpKbipgiM8hu0ZxViLNmFCYmIc6FBMKPRczTqym0T97\n70zcdBHUOC1gHYrpDHVubi5tbW3u5y0tLeTk5AC6T7GgoICiIj37tXr1ak6dOjXmOqPR2Rl4BhHC\n0+p0dCTuWngbjxz4HQ+/9ygL8+bwQb0+Le9Tcz9BXW8De5oP8vzxbeytP8y/XPDVsA3WiOzn4ksk\nYlFUhZquBvKT8nAodl45tYOVmStZVFzidyxvVuwCoDy1PCTxh/dz0YspK1qraWnpGfY9nO7fl9Hw\nN5axRHdspQAFUx6nGrsT+AzhrKkSM7IS3e2VAjlWJYT9n41tjlfw6J0dD4WXO5owjk9z6hcYsTzc\nZc2aNWzbtg2Ao0ePkpubS3JyMgBms5lZs2ZRU1Pjfr+0tHTMdWKNuRmzuW72lXTbe/igfh+z00r4\n1wu+xprCVdy24GZ+eMl3WJQ1n3pbIw22xkiHO21o6mvGqTqZnVrEJ8quRtEUXqh+xe/1B52DHGo7\nRm5idtjGhIea0tQibI4+2gY6Ih3KtEJkqAVRxVSZwNdoO4skScxIyvN7HbcI1mTyM5M40qff9g5E\nlPoreieCsU2HMva2vbPjobCeRBPGYBdNcQlqZ+xmqFesWEF5eTmbNm1CkiQeeOABtm7dSkpKChs2\nbGDz5s18+9vfRtM05s2bx7p165Bledg6scz6osvpdwyQlZbKxVmrMcke64rVZOHSgos41n6SD8/u\njRlxFu3U9Z4BoCh1Jityz+Ot+vfY33KIk21VpGvZKJqKBJjlkeXOwdajOFQHK/OWR2RcdygoSZ3F\n3paD1PTUkZOYFelwpg1CUE8TTnfX0jrQzoX5KyIdyqhomubl441uK8H/HvkTJknm31Z9w+913BcJ\nRoa6wZWhDkAceywfwReyygQy1LF4J8EbxbC3OPWfyljOUAPcf//9Ps8XLPC0UCwuLubpp58ed51Y\nRpZkbphzzai3hxdlzSfZksSeswe4sexaH8EtCA31vXo3i6KUmUiSxM1zP87De3/Fd978iXsZWZK5\npOAirpt95bBOHXuaDwBwQd6y8AUdYkrSigE43VPHBfnLIxzN9EEI6mmAqqn84ejTdAx2sjx3KZZR\nrtQjja83N7qFWr+jP+CJWG77gCYzIysRk6T/Yzsxy0coMtT+bdtbzEf7nYTJMtzyEbsZasHkMctm\nzs9bxtsN73O8o4LF2QsjHVLMU9d7BlmSKUjKB/Tx29eUbuC0rQbFqWGSZFr723jnzE72txzixjnX\nckH+crqHemjub+VE5ymKU2aRmzi2938qMSu5AJNkoqZHdPoIJ9GprARBRW9Yr3upnKojagW1jzc3\nyoWaU1MwEVhPWuP4ZExkpyUgY1g+AslQG1nkEGaox4nH+wIg1j3U7mNVhKAW+Meq/BW83fA+u8/u\nE4I6xCiqwhlbo88QFoBrSzf43EVwqk7erHuHV2re5I/Ht/CnE8/6JHBiLYtrMVmYmVxAQ28jDsXh\n89kIQkd0KitBUPnw7F7342jO/Pp4c6Pc8uFUnWgBZ6j1Y0qwWpBlyZ2hDqSXs2HHCPbno2qqZ/DM\neBlqL0uIEgIvdzThLiSdBkWJguBQlDKTvMRcDrYdpd8xMCWGgUxVzva34FCdFI3jVzfLZq4sWcfK\nvGW8WP0qbQMdZMVnkBmfQW5iTswJaoCStCJqe+tpsDVS6rKACEKLENQxzpBiZ1/LQffzaBaqjilk\nJVBcA2gCwcgqG0LaLagVx6jrDNtviIo2lQBa4Tmn0HmaLG7Lh8hQC/xEkiRW5a/gxepX2d96iDUF\nqya8LU3TeOTAo6RZU/hM+W1BjDI2qHNNA5yVMtOv5bMSMvls+T+EMqSooTS1iLd5n9M9dUJQhwnR\nNi/GOdh6xEcERHWGWvPOUEdvnKqmomqqnqUOYBSxIc7kcwS1fZyuGt4MOvRzaXcG98IokLsDio81\nJ7anw3mKEkWGWuA/Rsbzw6Z9k9pOS38rFZ2VfNS8n+Z+3wmUqqbyyP7f8c/bHmRX056oTpaECk+H\nD9FR5VyMiYk1YmJi2Ai7oH7ooYe49dZb2bRpE4cOHfJ578knn+TWW2/ltttu48EHHwx3aDHJB017\nAFiQMRcITbu1YDFVvLnecQYybtjjodb/7EySfoPIofh/rLZBXdAFOzPs27lDZKgNPJaP6dHlQxAc\nMuMzmJdeRlX3aXaf3Tfh37NjHRXux++f+dDnvcNtxznZWUlNVwN/Ov5n/n3nj3ij7u1pNQK9vrcB\nWZIpTJoR6VCijuyETJItSaIwMYyEVVDv3r2b2tpatmzZwoMPPugjmm02G7///e958sknefrpp6mq\nquLAgQPhDC/m6BjspKKzirK0Ene/5GjOYniLs+gW/p7YxvMbe2NkgY0MtVkO3PLhFrOSFtR/OJ0B\nZag976tMD0HNNOhDLQguHyu6DAmJx489wwO7fswbdW8z4BwMaBvHOk4CkGCO54OmPT6/FW/UvQ3A\n5svuY92sSxlUBnmu8mU+Ors/eAcRxSiqQsMIBYkCHUmSKEkton2wkx57dEwjjHXCKqh37drF+vXr\nASgrK6O7uxubzQaAxWLBYrHQ39+P0+lkYGCAtLS0cIYXc+xrOYSGxqoZ57ub2kezoPbOfEaz5cPp\nU5Tnf5xGJtokGRnqwIsSHSH6jLwzaONdJPhm6KP3PAWD6TQpURBcFmcv5N8v+haXz7yYPkcfz1W+\nzCP7f+v3hbBDcXCqs5r8pDwuLVxNn7Of/a2HAb1zU3V3DYuzFrBsRjk3z72Of175FUD/3Z8O+FuQ\nOJ0xbB8HWo5EOJLpQViLEtva2igvL3c/z8zMpLW1leTkZOLi4vjyl7/M+vXriYuL49prr6W0tHTc\nbWZkJGI2T6x5/lgz2WMBh6xnQ5bMmsueMwMAJKdao/a4u+U49+PEZMuYcUbyGKR+T5YoLSOejAT/\nYonv0r+nVrN+DhLi9eM1W+VhxzPa8XknYtIz40myJgYS+qg4evs9cSaZx/x8k+xW92NNUid0LqL1\nO3gurmset6BWTYpfsU+V4xOEltzEbG6ZdwPXlm7kqRN/4UDrEd5v3M2lhReNu25l92kcqoNFmfNY\nU3Ahr9fu4N0zH3Bh/gp3dnp90Vr38vlJuRQk5XOio4IB5yAJ5vhQHVZUEGhB4nTkvJxyttW+yZaK\n52jub+GGsmsiHVJME9EuH94FXTabjd/+9re8+uqrJCcnc9ddd3HixAmfSV0j0dnZP+b7ozHapKtY\nIScnhZ4+/bOxdduxD+qZtraOXlql6Dzu1q4e9+OOLhutiSPHGelz19Lf7X7c3NqNM8G/Gz0d3frd\nGFRobe1Fc2pggd6+fp/jGev4+gY8t4ybW7tJsQYnQ9xq83z2nd22MT/fji6b+7FTVQI+F5E+f4Fg\nd7gunhQzmgY9/f3jxh7I8QnhPT1IsiRyy7wbOd5RwUvVr3J+7lISLWNfDB9r1+0ei7Lmk52QxcLM\neRzrOMm+lkMcajtKSWoRc9J9k07Lcpfw99Ovc7TtOCtjsBWcN6IgcXwKkvP51sp/4rGjT7Gj4X1O\ndVVz7wWbkAasJFkSiDfH+wwoM6YVDylDoEGyNSmC0U89wiqoc3NzaWtrcz9vaWkhJ0efTlRVVcWs\nWbPIzMwEYOXKlRw5cmRcQS0YHePWvVk2e6byTRVvcgC+4nAzUa+3x/JhdPlwFSUG0oc6RL26vbdl\nH+ez9+4IosW4h1rFdXtek0ExM+gUlg/BxEiLS+HqkvU8X/V3/n76DT457/oxlz/eUYFFtjAnTRfN\nlxSu4ljHSf547BkANhRdjiTWzdrMAAAgAElEQVRJPussz9EF9f7WIzEhqJv7WjjTdxazZMIsm5GQ\n6BjspHWgnUNtR0VBoh8UJs/gX1b+E3899RLvNX7Id7f/t/s9CQlJkpBd/1dcHawM7l18B8tzl0Qi\n7ClJWAX1mjVr+MUvfsGmTZs4evQoubm5JCcnA1BYWEhVVRWDg4PEx8dz5MgRLr/88nCGF3M4VF0Y\nWWSz20MdSBFduPEWqoG0kgs3ExW1DtcxGYLabDJGjwc+KfHcx5PF6fPZjy2oHRPscjIVUTUFTYM4\nixlUk/BQCybF2lmX8H7jh7x9ZidrCle5i8XPpXOwi6a+ZhZlzXcX3C3OWkiaNZVuew85CVkszSkf\ntt6MpDxyE7M51n4Cu2LHarIOW2aqoKgKP9v/2zEL6pZkLxIFiX5gNVm5bcHNLM0pp26wjrbuLvqc\n/fQ7BtBQ0TQNFQ0ZmTiTFVmSOdZxkiPtx4WgDoCwCuoVK1ZQXl7Opk2bkCSJBx54gK1bt5KSksKG\nDRu45557uPPOOzGZTCxfvpyVK1eGM7yYwxBqFpPFk6GOYkHtLRADaSUXbiba3s+4mDG5Lm7Mshm0\nAAW1Tw/o4J1LxSdDPV5RoneXj9gW1IqmgiaREGdiQDGJwS6CSWGRzdw89zp+c+j/+Oupl/jyefcM\nyzKDp7vHosz57tdMsolLClfx8unXWV90uc+tegNJkliWs4TXardzrP0ky6awGDrVVU2PvZeFmfNY\nkDkXp+pE1VQy4jPIScgiJyGbVGtypMOcUpRnLWBtzgXjWtJUTeVb7/wH1d014QksRgi7h/r+++/3\nee5t6di0aRObNm0Kd0gxi92dobbwweEWiJs6GepojtMnQx2Q5cM3Q22RzaAElmlWfFr2RSZD7S24\nY97yoamgySTEmRlQzdjVwNqeCQTnsjhrIQsz53G8o4IffPgwy3IWc17uYmYlF7rF9fF2vf/0osx5\nPuteWbyO0rRi91yBkVjuEtT7Ww9PaUFtdDTZWLyWeRlzIhzN9EKWZErTijjeUUGv3UbKKBcuqqby\n9Im/0u8c5J7Fnx7xIm86Mb2PPsZxuoRPb5+TUw36FemQI3q9yVPFQ+2c4ERHY1mzt6AOcBtKyDLU\n3ncHxt6u9/uapAY0LXKqoaKAJpEYZ0ZTTDhUe8zbXAShRZIkPr3gkyzLWUz7YCev1r7Fjz96hB98\n+DDvNOxiwDnAic5TZMVnkJuY47OuSTaxMHPeiFltg1kphWTFZ3Ck7URUJybGQtVUDrYcIdmSxJz0\n2ZEOZ1pSllYCQHV37ajL/K36NXY2fcSB1sN80LQ3TJFFL0JQxzAO1Yksyew63Iym6qd6PLEUSZwh\nyr4GG8XHxxy4/9kY6GJ4qAPpZe1TEBnMDLXPxcx4gtp3v7EsMI0MdXycGVT9Ami8DL5AMB4Z8el8\nbsmd/PjSB7h38R2cn3serQPtbKl4jn997wcMOAfHFc6jIUkS5+UsZlAZ5GTHqRBEH3oqu6rpddhY\nlrN42mc9I8Vst6CuGfH9fS2H2Fb7FlnxmVhkCy9Vv8pggIOLYg3xTY1hnKoDi2zm3UNN4BLU9ijO\nWHgLuej2ek9ssIuxntHdwzKBYTtKACPCA8HH8qGOV5So71dTpWHrxhoahodaz1ADwkctCBpxJivL\nc5dw9+JP8/2LN3NN6QbizXp/+mU5E7drGIVk+1sOByXOcGPEvTx3aYQjmb6UpBUhS/KIgvqMrYkn\njv8Zq8nKF5d+hg3Fa+mx9/Ja7Y6wxxlNRLQPtSC0OFQnsmamrXsQOdXIUEdvds1HUEdzUeIEvd7u\nDLUrM20x6X9+E81QB7LeuNsNIOtuWIlQzSA7dF/3FO4mMBYqCpomkxhngiFDUA8Bon+0ILikxaVw\nbekGNhZfQftAB/lJuRPeVklqEanWFI62n0DTtAlluiOFqqnsbz1MsiWJucLuETHiTFZmJs+grqcB\nh+Jwd1Ppdwzwu0OPY1fsfG7xHRQk55OdkMnOxt28Wf8OawpWkZWQEeHoI4PIUMcwDtWB06V9slL0\nIQLR3I7OEcD460jik6GeQA9pj4faFPA2Qmb50LztNuNYPowMtWL0No/ei5/J4p2hRtEvgESGWhBK\nLLJ5UmIa9KKyeRll9DpsnO1vCVJk4aGqq4Zeu43zcsoxyRObgiwIDqVpJTg1xT1EB+Ct+ndpG+xg\nY/EV7qJXq8nKJ8quxqk6eaHq75EKN+KIDHUMY1cc2O1QmJ1EXo6F40yhDHUseqg13wy12WxCU6WA\nMs1qiCwfvncHxmub54pBDfyCYKqhaiqolnMsH6IXtSD6mZdRxp7mA1R0Vo3a7zoa2d96CIDlOcLu\nEWnK0op5u+F9qrtrKEsvwa7YeefMTpLMiVxV8jGfZVfmLWNHw/vsbTlIx54uJElCAsrSS7lu9pXT\nwgsf+0c4jRl0ONBUmcvOK3BnQ6O5v7O3qFOiOEPtneUfrW1eRWcl/7Xnf+i1e8Z0K+6iRP061iRL\noMmBWT4IjeXD4ccxud83zo0SuGVlquGToVZFhlowdZiXrreaq+isjHAk/qNqKgdaDpNkTmReRlmk\nw5n2GIWJVS4f9QdNe+hz9HPpzNXEnWPzkyWZW+Z9gkRzArW99dT01FHVXcNrtdt5fppkrUWGOoZR\ncIIaT1lhGvU9Z4GpU+wXzTYCh1fR3mgXKMc7TlHTU0eDrZGFrl6yRmbXKEY0mQIX1N4dNYJ5Lu0B\n3B0w3tdUk8/zWMQtqK0iQx1JTjf14JRk8Q9WAGQnZJIRl86pzmpUTZ0SGcKTnZV023u5eMYFwu4R\nBWTEp5MRl87p7loUVeHN+ncxy2Yun3nxiMuXpBbxX5d91/2839HPT/b+ijfr3iEnIYtLC1eHK/SI\nEP1/YYIJoY8SVdBUGbNJcrdqi2Zvsn2KdPnwjnO0FmqG6BqpI4jFsHzIMqiy3uvYT7yXDaaQdaiB\nZKhd+1UCb/s31VBR0VyDXQyLi8hQh59H/nKIHz6+e9zl2roH2PpOFUP22P1O+oskSczLKKPP2U+j\n7WykwxmXpr5m/u/o00hIrJohpiRHC2XpJdgcfbxe9zZtA+2syl9BqtW/ouxESyJfOu+zJFuS+HPF\nCxxtPxniaCOLENQxilvIaTIWs+y2GTjHaYkWSSbaji7ceNsjRuvZPOjUBfVIQtUs69XSJpOEpgXo\noSb0Hurx7DZOd9u8wNv+TSU0TQNJcw12MaG5ixJFhjrclM5I5XRjD80d/WMu9/grJ/jbzlp2HDgz\n5nLTBcM2UdFVFeFIxqZtoJ1f7H8Um6OP2+bfxJz00kiHJHBh2D7+fvp1AD4267KA1s9OyOKLSz+D\nSZL5/ZEnaOprDnaIUYMQ1DGKO3OqmjCbZKyuFm3RPDDFqYYm+xpsHH5lqO2uZT3vK5qCprky04BJ\nlkGTAxqMonktG8wBI96FiOMJfLcdJ8Yz1O7j0mQS4j0ZartTZKjDzYp5+sTAfRWtoy5zpLqdozWd\nAGzffwY1hid4+otbUHeGVlArqsIbdW+z4/SucZc9Y2viPz/6BX84+hRvN+ykorOKR/b/jm57DzfP\n+ThrCleFNFZBYMxOKwb038Ml2YvIm0AHmtK0Ym5f8CmGFDsvu4R5LCIsaTGKIeR0y4eM1dVDMpjj\nqoONt4iOZpHmk3UeRfgbWUxvv7WqKaDJmM26oDabJJflw79zomkamuQR1MGceunw+ezH3q5yTpeP\naL74mQyKcfHi8lAbRZiDIkMddpbNzUaWJfZWtHL1RcXD3ldVjT9vr0QC5sxM41RDN8dqOlhcmhX+\nYKOIzPgMsuMzqezy9VFXddXQPthBXmIOuYnZJJgTJryPzsEuHjv6pHtE9ZfOu4fyrPmjLr+r6SNq\ne+up7a1nT/MB9+vXlm5gXVFg2U9B6ClIyifOZGVIsbO+6PIJb+f8vGW8Xvc2B1oO0zbQQXZCZhCj\njA6EoI5R7OdYPqwTmMoXbry9u1EtqP2wfHgEtbeVQi9wM7mGLOhdPiRU/MtQG5lhTZWRZDWoHVvc\nNg4NFGns7RqC27BAxGrbPPdxuUaPa6ooSowUyQkWlpRlcfBUGx09g2Smxvu8//6RJhpa+1izJJ91\nK2by/cf3sH3fmWkvqEHPUu9s+oiG3kaKUmdyuruOn+//rc9vbHpcGpfPvJi1M9dgDWBI05G24/zx\n2Bb6nP0szlrAic5K/njsGf71wq+RHpc24jonOyqxyGa+tfKfqOtpoLq7lryknICtBILwYJJNbCha\nS5e9hzKX/WMiSJLEx4ou4/Fjz7Cj/j0+Oe/64AUZIDZHH6/X7uCywouDOoRGWD5iFLfVwChKdFk+\norl7xlTJUHtflIxW5Gl4qH184Sigynp3D8Bk0i0fmp9FiW5vs2IUmAbP8uHwaoU3ruXj3Ax1FN/1\nmAwey4eE1SwjayJDHUlWLykAYP+pNp/XhxwKz71TjdUsc+OlsymdkUrpjBQOVLbR3j0YiVCjirle\nPmqbo4/fH/kTqqZyTcl6rph5CYsy5zOk2Hmh6hX+Y9d/8t6ZD/y6SH6tdju/PvQHhlQ7t82/iS8u\n/Sx3LrsZm6OPx448NeI2eu02GvvOMjuthMLkGawuuIBPL/wk64sun1LTHKcbV5eu57b5N036HK3I\nXUp6XBo7m3bT7xgIUnSBoagK/3v4Cd6oe5va3vqgblsI6gjhUJ0+PYqDvv1zPNRms4ymylGdTfRk\nSSWfASbRhreIHj1D7fJQj2D5MMkey4cWgIfak6F2+eGDmqF2bVsxj9t1RPeCS2iqfhzR/J2aDIag\n1jQJkyxhlfSs6KBTiLRIcNHifAD2nvSd/PfaR/V02exsuGCWO3N9xfKZaBqiOBGPj/pkRyV/PLaF\nzqEurildz7WzN/LJedfz5WX38L3V3+bK4nX0Owd4+uRWfnHg0VH/rjVN4/nKv/NC1StkxKXzrfPv\n45LCi5AkiSvnXM7ynCVUdZ8e0Str9MSenzEndAcsiFqMlntDip33Gz+MSAx/OfUip7qqWZazmGU5\ni4O6bSGoI8DhtmN8d9d/8sCuH2EPUQsuu1eG2iRL7hZt0Zz5VbzasUVznP70yx7J8qFqqlucgaso\nUZVA0vwSpc5hGergZYbd21ZNaKhjinxVU/S4Nf3nI5rvekwGRTU81DKyLGGV40DTbxcKwk9WWgJl\nhamcrO+it1//3aw8083LO2tISbRwjZe3+sKFuSTFm3n3YCMOp/9Fv7FIelwaeYk5HOs4ydH2EyzM\nnDdsyl2iJYHry67iu6v/hUVZ8znVVc0bdW8P25aqqTxzciuv1+0gLzGHb57/JWamFLjflySJTy/8\nJNnxmWyrfYtq10AQg5OGoM4Ugnq6cknBKqwmKzsa3g97/c27Zz7gnTO7KEyewR0Lbw16b3YhqMPI\ngHOQ3x16nN8c+j86h7oYUuz0OcZuAzVRDEEtSyYkSdIL4DQ5uosSNY+VILoFtVc3knE91F4ZalwZ\napO3h9qV5fXjeBWvLLIeR/DOpediZvxtK5qqxx3zGWqjDkG/CIq3mEGxYgvR36xgfFbMy0HT4EBl\nGw2tNn7+7EGcisZnr1mo9wp3YbWYuGTpDHr6HeytaBlji9MDw/aRHpfGXYs2jSok0uJS+cyi20iz\npvDy6dep7210v2dX7Dx29Cnea/yQWckFfH3FP5IRnz5sGwnmBG5f+CkA3qp/z+e9k51VJJjjmZVc\nGKxDE0wxEi2JXDzjArqGutlVtzds+z3VWc2fK54nyZLI55fcRbw5Luj7EII6jOxs3M3BtqOUpZW4\np+eFakiEIeSM2WImk8vyEcVC1WMlMAU07CTcOMcZgqKoijt77JuhdnmovSwf7iyvPxlqrwsOfdtB\ntHycU2g4lqA2rCtoks+6sYbR5UPSZCRJwmoxgdNCn11kqCOF0T7v7QONPLzlAH2DTj57zQKWzcke\ntuza5bpo275P2D5W5Z9PQVI+9y6+nRRr8pjLJlkS+fTCW1A0hT8eewaH6qRjsJOf7v0V+1sOUZZW\nyldXfGHM7cxJn01BUj4HW4/QPdQDQPtAJ20D7cxJny2mIE5zrph1KRISv9v7FN/Z+UO+s/OHfO+D\n/3J3igk2mqbxTMVzANy7+I6QdRgRgjqMGJ7pG+ZcS0GS7gcMVccAw9srY0zl08VbNAtVxbASqIH1\nZg433haHkYSn90WSdwZbRT0nQy17fMh+XOg4vQoHR9v3RHGek6EeS6wrKLqY1mI8Q+0+Lv044ywm\nVKeFPmd/VH8/Y5m8jERm5iRT3dhDt83Opo/NZc2SGaMuW16SwamGbhrbpvdF0Oy0Yv5t1TcoTRve\ncnAkyrPmc0nhRTT2neXxo0/z448eod7WyJqCC/mn5Z8bt82eJElcWrgaVVPZ2fgR4GX3EP7paU92\nQiYbiteSak0CdCtRc38ru8/uC8n+GvvOcravmaXZi9w1BaFACOowYojneFOcuzVRqDLUbsuHS1Cb\nTNHvofbOfEa18B+nD7X3RdJwy4ekX9ygT0p0Z3n9EMchtXy4hs5o7t7So3cQUTUVTZXR3Nn1WM1Q\n65+37PqZtFpkNIcVVVNFYWIEWbVIHyzx8YtL2HjBrDGXvXyZnqV+52DjmMsJhnNj2bVkJ2Sxv/Uw\n/c4Bbp13I7fNvxmL7F+33Qvyl2M1WXm/8UNUTeVk5ylACGqBzifKruaX1z3I9y/+V763+ttYZQtV\nXadDsq99LYcAvctIKBF9qMPIgKuVWpwpjji3oA5VhloXRGZJP8WGvSCqhapbUMtofvZmjgROTdEz\ny5I64gWK9zn1FpsqKppPlw85IA/1uZaPYGaGFVXx8UX7ZflQ9YuBYHYbiSbclg+vDLXm1Ack2Rx9\nJFoSIxZbqHjooYc4ePAgkiSxefNmli71/AO0bt068vPzMZn0799PfvITampq+OpXv8rcuXMBmDdv\nHt/5zndCGuOVFxaxuDSLoryxrQugD4RJSbTw/uEmbr58NhazsBr4S7w5jnvKP83fTr/GhqLL3T5s\nf0kwx3Nh3nLea/yQo+0nqOisIsWazIykvBBFLJiqmGQTJWnFVHRW0ufoJymIv62aprGv+SBW2cLi\n7EVB2+5ICEEdRgyhlWCOI84U53ottBlqk0tQe4aIRK/4UVxCVVN14a9pWlT2JtXFpwTIPtlqg0Gf\nDLX+vm4R0ECVRixK9MdDbezLnaEOondZ0Zw+No6xOnfomXYLsuTycgdxYmM0YVywSO4MtQltQL8Q\ntjn6CXwAb3Sze/duamtr2bJlC1VVVWzevJktW7b4LPPoo4+SlJTkfl5TU8OFF17II488ErY4zSaZ\n4vwUv5e9ZMkMXvmwjr0nW7moPD/E0cUWRakz+dJ5d094/UsLV/Ne44c8V/l3euy9rMxbFpW/6YLI\nU5ZWQkVnJdXdNSwJovBtsDXRMtDGityl7kRmqJh2lo9eu41vv/c9dpzeFfZ9G0LLO0MdqrZ5htXA\n5M5Q60J1vJZokUTPfHpEXbTGaQxoGc1CM+T0nFPjPDi9pu552uZJ7iyvfx5qT1tBn+dBQNFUUD2e\n7rGGxhhecOPWbzDb90UT51o+4iwyOPW/274YbJ23a9cu1q9fD0BZWRnd3d3YbKHrlR8uLjtPb+sm\nbB/hZ2ZKAaWpxTT3651WhN1DMBpz0ksBqOqqCep297UcBGBF7nlB3e5ITLsMtSRJ2Ox9vFn1PuXn\nBbep93gMOYewyGZMsok4c3gy1GYvQe1dRCabou9aymgr5+keoWAi+m7RKqrT9VlqI2Zyh0bIUHta\nsOk9jcF1kROAD9nptQ1NlYLqh3fbbdTxM+ZuL7jJhJNYFtS+lg+r2dvy4WmdZ7P3caLzFCtylwa9\nr2k4aWtro7y83P08MzOT1tZWkpM91ooHHniAM2fOcP755/PNb34TgMrKSr74xS/S3d3Nfffdx5o1\na8bcT0ZGIuYJWi9ycvzLTJ+7ztI52RyqbMOORGHO+FaRUMUSKqI5lmsWrOWXux8HYPWcZeQkhS/W\naP5cIkk0xpKSvgj5oExtX13Q4tM0jYMfHibeHMfa+SuxmsfOUE92v2EX1KN59Jqbm7n//vvdy9XX\n1/PNb36T6667Lqj7tw+YUHrTqaAam72PZGvS+CsFiUFl0G31iJND66G2O12CWjba5kkesaQ5sWAJ\nyX4ng6qpoJm9hL8TQnyLZiKoqC4PtTbiRMfBEYoSPRlqz2AXWZaQJtKH2pXFD6aHWrfYyF4WlJFF\nsqZpaK7jt8pmXVDHqIdaHVaUaEIbIUO9vf5dXq19i4KkfAqSY8dSoGmaz/OvfOUrXHrppaSlpfHl\nL3+Zbdu2sXz5cu677z6uvvpq6uvrufPOO3nttdewWkf/u+3snFgf75ycFFpbeye07upFeRyqbOP5\nt05xy7rJZ0knE0uwifZY5ibMI8WSTJIlEanfSmt/eGKN9s8lUkRzLLOSC6nqqOXM2Q6spslrlNqe\nepr72liZt4zuziFgdL3l7+cylugOq6Aey6OXl5fHE088AYDT6eSOO+5g3bp1QY8h3mpC6cpBTunk\naPsJVs04P+j7GI1B5xDxZn00bpw5tF0+hs4V1AH6dSOBit42T1N9fbx2xc6TJ/7CFbMuoSS1KJIh\nAobfWEaPeKQMtVfbPHeG2hgbLusdV1wY2U+/MtTuaYYuu0kQ/fDeLQthdMuH24ajyVhcP3ix2uXD\n+DuRJG/LhytD7dWLum2wA9CLsKYyubm5tLW1uZ+3tLSQk5Pjfn7DDTe4H1922WVUVFRw1VVXcc01\n1wBQVFREdnY2zc3NzJo1dveNcLNiXg7JCRbeO9zEjZfNxmKeuncSphoWk4VvrbxvSt+9CRUv76ph\nyKFy02WzIx1KVFCWXkJtbz21PXXuIlhN0zjafsIn+ZgWl8bstOJxv1N7w2j3gDB7qP316D333HNc\neeWVPsUvwSIx3kK2VALAodZjQd/+WAwpQ8Sb4tA0jbqmAfdrE+WV02/wVt07I+/L5eO1yLoAMDzU\nEDoBpGn+jdAeDaMLhmH5MLZV13uGPc0H2Nt8MChxThbvbiQjTZ70PqcDDv08eHuojbZ5ABL++6Hd\n9hJj30E8j0bnDm2ciy7vGOIsRuyxKaiHt80zoTmGZ6g7B7uRkEi1Rs9t1ImwZs0atm3bBsDRo0fJ\nzc112z16e3u55557sNv17/NHH33E3LlzefHFF/n9738PQGtrK+3t7eTlRV8XB4tZZs2SfGwDDp7d\nUTks+y4ILVkJmSNOVZzOqKrG33bW8vKuGvoHY/M3NFDKXD7qSi8f9btnPuDXh/7AY0efcv/33/t+\nzf97/0GerXiB6u7aEf+e9e4eh4g3xbMoa35Y4g9rhtofjx7As88+y2OPPebXNifix1tRMps3bQkc\n76ggIzMBsyn0H4OqqQwpdsyylUe2HuZQXR3x54FTUibs23nr3XdJsSZx6/nXDntv8JT+D19SfDw5\nOSnIVo+VIjUjnpzk4P/j//eKt3jm8Iv8z7XfJzU+sO2rmqq3yvOyHRhxnnHq50e2em63RNIDpqEX\n8CGBhmNYLKZmz2NF08+vo8clwDSJ3JwUMlL1bKZJMqEBSSlWn+2MdHwJPa5bYC7hq0la8Lxm+I4T\nT0w2j7htd2ZWlUiK149BNksBxxFpD9+rp3YwP7uM0ozRM6lJfbp4Npss5OSkkJWZ5LZ82GW7+xh6\nnT2kJ6SSn+cRDJE+vomwYsUKysvL2bRpE5Ik8cADD7B161ZSUlLYsGEDl112GbfeeitxcXEsWrSI\nq666ir6+Pu6//37efPNNHA4H//Ef/zGm3SOSfPziEg5Xd/DGngYyU+K5alXk73YJpi/Nnf0MOfSL\n9oqGrhGnfU43ytJKAKjq1vtRDzgHePn0a8SZrHyi7BokJECjwdbIgZYj7Gh4nx0N73PHwlu4aMZK\nn23V9TbQOdTFqvzz/e6dPlkiWpQ40lXF/v37mT179jCRPRoT8eMV5SShNuQyFF/LrspDLMicG/A2\nAmXQOYiGRlVdH/ZTbVjjdXHUZbNNyM+kaRoDjkFMkmnE9Q3Lh6ZAa2svPf12t1hqaevGNBD829MH\nGk4w6Bzi5Jm6gK0ZRt9sw84A0OqKs7WzG4Cevj5aW3sj7gFT0EekS+hdPs6NpaOnx/3Yrthpbe2l\n1eZaRpPp6urHOaQfr6RJaEBHl41Wq77MaMfX5RLlmqr3gHaqzqB9Dgqu3tqui5n2LhuticO33WPX\nX9M0Gcl1nvoGBwOKw9/zV9tTz9Mnt/KFJXcFNbvVa7fx2L4trMhdyj2Lbx91uc5uz8VDa2sv9kE7\nKGYkJDps3bS29qJqKh39XRSmFLiPKZDvZ7QJb+86FoAFCxa4H991113cddddPu8nJyfzm9/8Jiyx\nTZakeAvfuOU8HnxiL3/eXklaspXVoo2eIELUnvX8Rpyo7RSCGkixJpOXmEt1dw2KqrCtZjs2Rx/X\nz76Ky2de7LPsrfNuZH/LIf5w7GlOdFQOE9SVriEx4cpOQ5gtH+N59AB27NjB6tWrQxrH3JnpKF36\nfg+3hcf2YRSqaYqJO6+azwXz9VZOg86JWT6GlCE0NIZGWd/o8uG2fPh4qENze6ljsNO178B94R4r\nwXCvt7G9ULUYDARPP2nfftneGB5qzWl22waMDh2a6mmbByC7urD4Y99wnzdXFn+kgsiJYBQaoknu\noTGjfUcU1dvyYUxsDI0n/0THKep7z3Cqqzqo2x1wTTkc72/Pbflwe6hNgIRVindbPvoc/Tg1hYy4\ntKDGKAgNmanxfP2W80iIM/PYy8d59cM6PjrRwtGaDlomWCwpEEyEmnMEtUBnTnoJQ4qdQ23H2F7/\nLhlx6Vwx69Jhy5lkEyvyziPeFE9db/2w92t79NfCWXcVVkE9lkfP4PDhwz5ZkVCQkRJHrmUWKGYO\ntx0Pi5/OLXwVM0W5KcRbrGjaxIsSDVFgVx0j9ms2unwYlbKmMHioDUE9kWPyFmqa5tub2RDSoSrg\nDIRzRS0M75dteKg1p/h4puIAACAASURBVBUVBVVTvY5P8hXURlFiAF0+jIuOYA3p8fZFuz3UowyN\n8e5WEuf6bgWz24g3xnk3suLB3u543yfj+2e0brS6POMW4rG5BHXnUBcA6UJQTxlm5iTzlZuXIEnw\n5+2V/Pr5Izz8zAH+9bcfsOvo2UiHJ5gm1DX3IgEl+SnUtdiwDYze+386UZam+6j/dPxZnJrCDWVX\nj9rxQ5ZkilNn0tzfSr9jwOe9mp56ki1JZMVnhDxmg7BaPsbz6IFe2JKVlRXyWMpnZ/NORzbtprM0\n9TWHvN2VJ0NtxmqRiTObwWGacFGiIahBFwjx53QYMCwUVpPhAw1thtqu2N0iYyLC153B9RKqRn9j\nT4Y68j843sWFoF+Indsv2y2oHVaI78epKr6DXUweQW2S/B8j7t3lQ8+OB2fwjXJO9xAA5yjTD91F\nmKqM1RL8iY3eDKkuQT0UZEHt2q7x/9FQhnX50M+VWYun29GFqql0Dep2JCGopxbzizL43j2rON3U\nQ9+Ag75BJ9t21/HEtpOUFaSSmxF7Y+UF0YOqadQ295KXmcjyudnUnO3lZF0n58+PtfmrgWMUJg4q\ng5SkFnF+3rIxly9OncXJzkrqehvc9t1eu432wQ7KsxaEdTJn2D3UY3n0AF566aWwxFFemsWO6lzI\nOsue5gNcn3xVSPc36JWhjrOYsFpkGDSPOZFuzO0pHkE9NIKgtqsONA0srhZtsuTVhzoEGcWOwS6v\neAK/SHALSq/WbcZrdtdnNJ4ACgfe1g1J0gWtU3X6jDR1n2un0VbO4ekzrcr6uXBhCGp/MtSOc7Lj\nxtTLybaj8ukeYrTNGzdDLZNgnpoZ6iE/L9CMYzVJRpcP/f8mLQ5N0uh3DNA1pAtqYfmYeuRnJpKf\n6RHOuekJPPq3Y/zupWN8+9Mr9GFYAkEIaOsaYGBIYWlZCguKM+Dd0xyvFYIaICs+g/S4NLqGurl5\n7sfHFcTFqXpheW1PvVtQG3YP471wMW1/McpnZ6F05CFrVj5o+ihkosDAPexDNWG1mPSpa6ppwiLR\nO0M9OIKAdSgOfV+uDiiSJLmFVygyiobdAyaWSfa1UngmJYL/AigceMc5Wou5IcUOqn5+Qb8gMNaT\nJNnnB0J2Z6jHPycOV9bYYjIPay0YKN7fH++BMUY8o3qoNc+ycVYLmubfUJqJYJzvUFk+xvPkG1l6\n46LHyFCbVH04k83RR6dLUKeLlmBTntWL87moPI/qxh5efP90pMMRxDCGf7o4L4XSGanEWUycqOsa\nZ62Raens58X3TsdM6z1Jktg0/0Y2zb+J2a6uH2NR4iWoDWrc/mkhqMPCjOwkUhMToLOQbnsvR9tP\nhHR/Q16WjziLrGe7FBOOIAjqkTLCDtd4bN8hIibPe0HGW1BPJEN9bo9l8IhMw74SDUWJbvHs1RHj\nXPE5pAwhqWafAj93gZvm+yfnzlD7IYyN82Y1mbymXgYuZis6q7j/nX+norPSN35NdrcXGk1Qe2eo\nrRajF3doM9TdQRfU/n2fHOpIRYmAot+NsDn63BlqYfmIDW7fMJ/stHhe3lnLyTpRKCYIDbXNLkGd\nn4LZJDN3ZhqNbX102/z/t1PVNN7YU8+/P7ab5987zY4DZ0IVbthZkr2ISwsv8mvZ9Lg00qypbhEN\nXhnqFCGow4IkScybmUb/mRkAvN+4O6T7GzQEsGLGajFhMctoqgmn5phQUeSgt6B2DhcGTtUBquy2\nfICnuCoUHupOH8vHxIsSNVUePilRjZ4uHx7x6WVN0c4V1HqG2jN10OkWoufaMzyWj0Ay1JZJ+eFr\nuusAaOxrdu3bc5Fgddk4RrvoUrwufMwm18TGEN3dMb5HvcH2UCv+eag9lg/9HCXE6RcbqkP/jPoc\nfW4PdVpcalBjFESGxHgzX7i+HEmSePzVk6hiAIwgBNS5M9R6U4aFxXrhnL9Z6pauAf7zqf089cYp\nd5F7XXN0jBOPBMWps+i299A11I2madT21JMdn0myNfjDAcdi2gpqgLmz0tEGUsk053G0/YQ72xQK\nDFuGCQuyJOlWDMWMhjahjLE/GWpNlfViRBdyANnQQGmfpKD2bptndL4whNqQnwIoHCgjZNLP/TwH\nnUNoitmruNLjoZa8ihfB4891Kv5kqPVlrGaz224ykeyw0ZnC7roQ8+6wYlRTO0ax1xjLyrgmPmpS\n6DLUrvPd5+wP6l0Vo9jRoTpH7JBj4BbUsn7O4q0mTLKEc0gX1kaGOsWaHLbBAYLQU1aYxuryPM52\n9HOosj3S4QhiDE3TqDnbS256AomueRQLXIL6uB/t8xxOhZ88vZ+K+i6Wz83moc9dREKcmfqW4VOn\npwvePuq2gQ76nP1h909DEAW13W6nqakpWJsLC3MK9du0aYNz0NDY1bgnZPsy2uZZJP12sdUiuy0B\nE8m8Do4jqBXVCaoJs9lziuUQZqh9PdQTyVB7bAdmw3bgEmqGuBtPAIUD47PTh6BIPq+B/mM5pAyh\nKh4PtdM7Q32uoHYdqz+C0dhPnNniVWAa+Lk0BLXxvXFnxzVJ3zZgH6XLh7GshEm3E2kySpDa952L\n94WZzR68fyy8v59jfVfPLUqUJImkeDP2QZegtuse6mgvSJyKv82RZuOFeu/a1z6qi3AkglijvWeQ\nvkEnRfmeoU7FeSkkxJn96kf9xp4G2roHWb9yJvfdtIS05DiKcpM5297PkD20tWDRiuGVrumpp7an\nzue1cDIpQf3b3/6WJ554goGBAW644Qa+8pWv8LOf/SxYsYWcWbnJmE0yvY05WE1WdjXtDplgMzLU\nFtklqM0mNEUXVxPxHA+c0+XjXJya7qH2sXwEYC8IlI7BTrcQntRgF1XGLPsW6g35CKDIFiZ6e4jP\ntaaAno3W0NCcXpYPxem2hYxm+XCMImB99214qM1eYn4CGWqXTcH4XJ1edpu48SwfhshEH1CjaXLI\n/ma8v0fBLEz0/j4NjfF9Us6xfAAkxlsYGnBNHB1ow6E6SI+LvoLEqf7bHGlm5SZTXpLBiboun4l2\nAsFkqT2rJwcMuweALEvMn5VOS9cAJ+s6OdNqo6HFxqDd93e4t9/O33bVkBRv5oZLSt0F7rPyktGA\nhrbpmaUuSpkJQF1PAzWuIS8laeEb6GIwKUG9fft2br/9dl599VWuuOIKnn32Wfbt2xes2EKO2SRT\nnJfMmeYhlmcvpX2wk+MdFSHZl9FKzSp7Z6h1AToRi4Sv5cN3fU3TUDTD8jGCoA5yhlpRFbrtPeQl\n5rjimUTbPM0jqJ3ntM3TH0fW9uHJ5o5clOg+Fz4eascwP66BJYAMtWH5iDN7PNSTsXwY58n77kC8\nxbB8jJahNqwrrn7aaggtH15iN5iC2tvOMlZRsHHOzF52jqQEMwN9+jk809sIRGdB4lT/bY4GrnRl\nqbftFllqQfDwLkj0xvBR//ip/Xzn97v598d28+3f7PK5oHvx/RoGhhSuv6TUbRcBKMrVt1XfPD0F\ndaIlgdzEbGp766nprkOWZGYmF4Y9jkkJarPZjCRJvPPOO6xfvx4AVY3sLflAmV2QhqppzLYuBeCd\nhp0h2Y+RoY6T9X7Rk85Qe7fNO2eEsqqpaGi65cNHUBtjroMrgLqGelA1lfxEvYfmhDzULlEnI3us\nKZrvYBf9caQz1MOHoHh/nsa50BSPz9nh3eVjmIfaf1+7IXzjLZYJT720K3b6HPqIZXeG2uvuQJzZ\n6op5HA+1ZMIsB3cE+rBYvcRu91BP0Lbrm6Ee/bt67uhxgKR4C4pd/4esyVXUGY2Wj1j4bY405aWZ\nFOYk8dGJFjp6BsdfQSDwg1qvlnnerFmSz8YLZrF2eSFXrChkzZJ8evsd/PipfZys6+T/s/em8XJc\n9bXoqrHnMw86o2bJGo1t5NmyMY4xBoK5JLGZzIt5IeRl4pHLfcTv8hwCdpz7w9yB/JJH/BLCHAMx\nCRiMJ2QbW8YaLUuWZM1HR2cee+4a9/uwa++q6q7u02fQkXTU64uOuruqd1VXV6+99vqv//BkDi/u\nH0BbYwTvuspPFnsdtfvs5eyjTvQibxZwOnUWXbFlZbsrnk/Mq5ImkUjg05/+NIaHh3HVVVdhx44d\ni9qVZiGwqpNW52cmo1hZtxxvTbyNsdwEWqML262ReZ7DUqmHei4EtJKHmpMhIkKRA5qILLBCzfzT\nzZEmKKIyr6JEARJEbvnwp3wAFz7pw5vZLPC24V6FmjXwkfjn61OoRT+hlqXqowzZPkKKUrYgciZM\nBTTg8a4ORJTKlg92rJJAC/RoC/TzQ9S082T5qPZ6YjnUsuczi4VpHKIkSPyabQhffIR6KdybLzQE\nQcCd23rwzV8cxfN7z+H33rXmQg+phksQ0xkNOgSooKvHfcMpNNeFkIiqvtdFwwrue/da32NbVjXj\n8Z8dxmNPHEBnSxSWTfA7t64uaTrU2RKDJArov6yTPrqxe2Sf8/fi+6eBeRLqxx57DDt37sTVV18N\nAAiFQvjbv/3bBRnYYmG1Q6hPDSax/YYbcPpwH3498Br+09r3L+j75M0CiC1AVSlhUWSRJkFgIQi1\nf3vDo6J6v3iyo1CX64I3VzALQVO4ASFJnWNRoqsG8ng/FpvnUaXncq4WErxBCxHpKgD8pJa3mLdl\nX+Ggq1D7b4SK85mUa/Xte29ighAgLCtuY5dZfpZTniQb10PNCg1Fp+GQUL6xi0ehliSa8mGfh6JE\n0ylATahxpPUMUgtYlFitQs2uPz+hVgAIiEgRZEw6povR8rEU7s0XA67fuAz/9tIpvPTGID5w4woe\nnVhDDdUgVzDx1/+yG9MZHcuXJXD12hakcgauWttS1fbXbmhHNCTj735yEGdHMljTVY9r1reWvE6W\nRHS2xNA/loFtE4ji5Td59hYhLq9bfP80ME/Lx+TkJBobG9HU1IQf/vCHeOqpp5DP5xdqbIuC5vow\n6qIKTg2lcFXbViSUOHYO7V5wJbRgaoAtIyzTG7KqSB6Fen6Wj+Lt3SQKv+WDe5OrIG+zAVOom8KN\nCEnqHBVqZvmQSqwp3s9iro1wFgqmx7ohVPJQe1I+DNv0FLj5f5BlyZ9oUgmWbdGsaNXTInwBFOoS\nXzQRyzd2Id6iRFqYSWDPKUu9Ethn3hqhK0ULqlD7rqcKRYlOsaV3VSEapp9XSIzwxy5GQr0U7s0X\nAxRZxLuv6UZeM7Fj/9JpnFHD/JHKzvxb9OTLJzmZ7h/J4Ce/ph04VxT5pyth86pmfP6+q3DV2hbc\nf9f6sitNvW1x6IaNkalc1fteSuiOd3J73oVI+ADmSaj/8i//Eoqi4PDhw/jRj36E97znPfjKV76y\nUGNbFAiCgFWd9ZhMachkLdzUdR3yZh4vnH0Zz57Zga/t/Qe8fO61eb9PwdJALNp2HABUeX6Wj7xV\nQEyOOdv7CTUv4rNFKJ7YPImncJw/Qq0uhEIt+q0UepWK4mKAEU1JkDxeb5fUejtienOovVYJL2Yz\nyTGJBRCRduwr6iZZLdhqAh2rP4daEpwJmC1Vp1A7lg9g4duPs2u4MdQAURCRWsDmLtVeT+xYZc9n\nFos4K0wI88cuRkK9FO7NFwvefXU3oiEZT/+mD3ltabR3rmF+2HN0FJ/9+it4tkLB6qnBFHbsG0BH\ncxT//f+8FY/9yU24791rcc36Vtywedms3m91Vz3+9MNb0d0aL/uaHseTfbnmUSuSgpV1y5FQ4lgW\na7sgY5gXoRYEAVu3bsVzzz2Hj33sY7j11lsXXKlaDKzito8Ubu68DqIg4qnTz+I/Tj2Nk8nTeP7s\nS/N+D83SAKftOMCKEmX3OdAf8McPfhu7hitX4xNCkDcLyGeCLSPeVtKBlo8F91Azy0cjQlJoXq3H\nJUHiBMa0LFi25SOsF7wo0aNQ86YsXoWaFYh6Uz4sMzCCDQCUWSnUJv9MXf/2bBVqavkQIHiKEt1s\naVmiqnM5b7ZZTL7n6OWeCWxsISmEOjWxwB5qT2pMFUWJUrGHGoDsEOqYEsV02sTPXj0N07p4iv6W\nyr35YkA0LOM91/UiWzDx/J7+mTeoYUmDEIKndp4BAPzoxZM4MVDaEM6ybXzrl0dBANz/nvVQZAn1\nMRV3buvBH39oC1rqIyXbzBe9bU5h4mWa9AEAf7DlE/j8O/+0JJ52sTCvd83lcnjzzTfxzDPPYPv2\n7dB1HanUwlXjLxY4oR5KojHcgPetvBNbWjbi41f8LjY0rcNEYRLj+ck57581+yBO23GA5k5K8BPi\nicIU3hg7hD0jb1Tcn2EbsIkNS6Ne2nJFicWdEln8VzWZx7PBZGEKMSWKkKRClVSYxJp1koib8iF5\nmp0YPvIDXPiiRD5Oj0JtBXmoLa+H2uDnvKQocTYpH8QCsWn+s0jKF5juGXkDT516NnAfTKFuDje6\nlg9v90Nm+Sjjzba8RYlObB4b20JC54RaRZ0aR0pPLRghrDbXnKViKCUeakCyQwCoOv3i/gH85Nen\nMTieXZDxLQSWyr35YsEd13QjFpbxzK5+5AruNWNaNvqG07XJymWEo31TODuaQU9bHDYh+MZ/HEIm\n77+PPLf7HPpHM7h5awfW9zYuyrh6eNLH5VuYmFDjaI4szvkOwrwI9QMPPIAvfvGLuPfee9HU1ISv\nf/3reP/7F7aYbzGwsqMOAoBTA/QH564Vt+MzW/833NC5DZubNwAAjk2dnPP+DdvkMXaMUAOADNaV\njv7Ap53Cq4xR+YeZ+actUwZsuSQ2jxNmW/I1dlEkN3VioUAIwWRhGk1hehGHnBST2VozvB5jt9mJ\nxQkP+70qJtiLDa9CKwY0yvHmUBPieqiNAPsAAMiyCFJllrNFbIAIkCWRv3fQxGVH/yt4+szzgWR7\nqjCNqBxBXSgBzdJpZrnHxkItH2LZYkfv8bPGLt7HFwrsPKqSijo1AcM2UbAWJrqs2k6JFiwQ4iax\nAK7lAya9zhtD9ZhO0+9fLLz4MU3lsFTuzRcLIiEZ771+OXKaiWd3U5V6IlnAo9/bhy/9y2783ZMH\nkS1c2HtTDYuDZ5zP//73rMcHb1qJiZSGf/75ERBCkCsYeOXNIfz7K6cQjyiLmgwTCytorgtftlnU\nFwPmVbJ899134+6778b09DSSySQ+97nPXZLRTJGQjM6WGM4Mp2HZNvfwAsC6xtUAgGNTJ3Bj57Y5\n7d+NUnMtHwDtmmjAJQ9sWTujVybUPOHDkkEsqbzlwxZ9rcdloXLTjrkgY2Rh2Aaawo0Yncphatrx\nPds6oqh+Wcu1fIge1dZ0CY+pAop+wRVqy6OkA4CJ4qJE97N2CwdN13tdrFA7Wc7VeKFtUA+1JNLI\nPoLgrpd5M+/8W0BCdT13hBBMadNoiTQjJIVgE5u2ReeFhq6NoxzB9yrtfg/1wq566JxQK6hT6QpS\nSksjIs9/qdTnoa5Q5GrZFkAEX8U8s3wQUwFkqlD3Z+hnXh9XA/dzIbBU7s0XE26/ugvP7DqL5/b0\nY+3yJvy/T76JbMFEc10I+4+P46/+eTc+c88mrO68+Dz1NSwMBsezePPkBNZ012N1Vz1WdtTh7f5p\nvHFiHH/9rT04N5qBZRMIAH7/vRsQjyzuJLu3PY79x8eRzGioj4cW9b1rmKdCvXfvXtxxxx1473vf\nizvvvBPvfe97cfDgwYUa26JidVc9NMPC6SH/cklHrB0JJY5jUyfmvKznbfYR8ijUrGsiI2Fph1Bn\nZ1KoHaWOcEJdpiix2EMtLXwOtVuQ2IBfvn4WZwadpiHm7HzU/sI4ZvmweGYwMeamfC80vF5vMcCu\noc1g+ZCLCDVL1ahOoaYpH5IoVGwIw1YwvEkw7P+apaMxVO9bSeCFhqIEWRJAKijUzH8vizIkSZxX\nC/RKYNewKqmoC9Fim4XwUVP7lc4nRDN6qInom2AzFZo1d2kINSCZ1RGPKCXZsBcSS+nefLEgrMq4\n+/rlyGsWHvv+PmiGjU/etR5/+5kb8ds3rcBkqoBHv7sPr7w5dKGHWsN5wrO7aRHie7bRWDZRFPDp\n396E+piKvuE0ulpi+PCtq/DIH16P6za2L/r4ehwf9eVamHihMS+F+mtf+xr+/u//HuvWrQMAHD58\nGA8//DC+973vLcjgFhNXrmnGywcGsf/4GNZ0uQqDIAhY17gae0cPYCQ3VrF61LIt2MSGUtShhy9V\nezzUAPjrihXqgqXBsE3elroYnChZCmDJ0Cx/TI7JPdSSz0OteIjqQoEXJIYa0J/S3OSSWcbb8RQM\nUXKzmT0KNXGW2C9463Fuj5BLHgM8XSstydcp0Sxj+ZBEZrGY+TOxHYLns3wEbMcU6kIRoWb+6YZw\nA2+/rVlaoEJNQGDZVomizgk1S/lg3SLPk4da04C68MIRapNY9Ng0BWLIqkiobcdiI3kUahabJ2ba\ncOXyzbimfSt+njmCxrqLSw1aSvfmiwm3XdWFHfsHIEki/vADG9HrJCvcc8sqrO9pwN/95CB+/OIJ\n3LhlGcTaisCSQjKrY+ehEbQ1RHw50vUxFV964FpohoXWhoUvNpwNepwW5GdHM9i8amGb09UwM+ZF\nqEVR5DdsANi4cSMkSaqwxcWLTSuaoCoi9h0bx+/cutq3PLq+cQ32jh7AsamTFQn1/3rjH5Ez8vjC\ntj/3ERFXoZZ8CnVIpD/CTM31EoaMnkFjuCHwfRihJpYMYkswbMNHfso1dlGc3OCFXJ5nvu86NYGp\ntAaismi+2fkJTY+HmkfJ2R4PtaNQX/CUD2/HQyfUwfLF5jkTANv9atFOiWzC4P/KSZIAQqrzUNuw\nQIgASRLKdr00LIN//jnTnzvMMqgbQw1IOg1eNEv3RQHKkkuSTWLxJjv8+C2/h/p8p3w8uaMP/+kW\n2j1sIZq7+CxEoQK0CteTBRsgos/yIUsiQqqEQl7Ep7fcD92wkNNMrIhVnyu7GFhK9+aLCSFFwpc/\ndS2WtddjYsJ/PW5Y0YSr17bi1UPD6B/JYPkssoZruHiQLRjYd2wMu46M4lj/NJrqwuhpi0M3LJiW\njd/a1lPSOKUudnHYvXgL8su4Y+KFxLzWKEVRxDPPPINMJoNMJoNf/OIXl+xNW1UkbFnZjJHJHIYm\n/IrvukZaWHBs6kTZ7XNGDiemT2MwO1wSe+f11aoeD7UqyyC24FGo3Rt0xigfzs6VR1OmLa7hV245\noXb8tgys4GwhLR+MtEWUKKYz2pyb1TDFVBZdy4dpm+5xMYX6QnuoiWvdkMRSUuttPe6NKWSEma0S\nMMgis3xUjlyziU0LWwmzfLBz5CeyeU/hXqlCTUk0tXyE+Hh53rLoFiUWHxcDe0yWZBqxN8eOjTOB\nr0xYEgpZep6T2vxTKvh+jZlXPOiKgF+hBoB4WEY2T4836TR3qI9dXAr1Uro3X2xQZKlsJzqmCh48\nNbGYQ6phgfDvvz6Fz/6vV/DNXxzFW6cn0VIfRjqrY8/RUbx5cgKxsIybt3Rc6GGWRUt9GJGQdFlH\n511IzEuh/tKXvoQvf/nL+OIXvwhBEHDllVfir//6rxdqbIuOq9a1YO+xMew/PobOlhh/vCXShMZQ\nA45Nn4RN7MCMw1PJPv73L8+8gGuXXc0JFyM2xPZ7qEOKDNgSV7B9CrVR/gvhV6jd6D1WsMUVR8g+\npV2SBMAQZ8wu1i0dw9lR9NZ1V3wdAORMSvxVQUUmb0Cqm1uzGlf5pN5cQqiFpoQAzbBfy7bwldcf\nw+aWDfjw2g/MagzVwPCouXBObYmHmlCSHFUVGCiyfIj+a0dyCKw9w2fC38MWIUkiz8AuVra9vuli\nD/V0wW0Rz7zvxQq1JIqe5I4gQu1aV5j/u/gcLAT452xLsDT62S+E5UMrthDNYPkgRZNSAIiGFYwn\n6UQymaHbN1xEBYnA0rs3XyrYtLIJAoBDpyfx/htXXOjh1DALZPIGntrZh0RUwR3v7Ma2De1oa4jQ\nJKuUhv6xDFrrwwipF+/EVBAE9LQlcLx/GppuXdRjXYqYE6H+6Ec/yokaIQRr1lAFN5PJ4Atf+MIl\n69O7ck0LREHAvmPjeN8NK/jjzEf9+vBeDGaG0Z3oLNmWEerO2DKuUt/gpIIUfAq1pyjR6ZboFiW6\nJDpbIenDm/LBFGrN1ABHJGOxeMVtriXRKTibgfw82/cinj7zPP6f6z+P9mhrxdfmDUosTN15L2tu\nhNrrzWXJF6Zt8iV5RoBm8mZnzRxG8+M4kzo/DRh4yoUgAyIryPN7qEWiABAQj4QwaQswHcsHsQVf\ngRsAxzYhwELlz4QrwESELApc/S4mvXmPzSNfFDM36XioG8MNnqJEzZ0kiDIU2SXJQQ2ATI+a7fNQ\nnyfLB2wJRoHWGiwEoXYtHwpAZiLUpSkfAE366B+ly7/JLEv4uDgU6qV6b75UEI8oWNlZh5MDSeQK\nJvfc13B+kczq+Okrp/GBm1agYY7fxTdPjsMmBHe8s7vk97+5Pozm+nD5jS8i9LbHcax/GufGM7XE\nmUXGnL7tn/3sZxd6HBcFYmEF63sbcKRvClNpDY0J94u5vnENXh/ei72jB8oQ6jMQIOCBzR/Do7v+\nB572qNRusw+/h1pVRBBL5nnAXsKQrpD04U/5oB9hwWOxYPaJkjbXPBKt8vL8ucwgAGA8PzkjoWaW\nDy3vEEV75vSEIHCiJjmE2qRKumv5YJF/lT3UOccqk6tgmZkP2DgVUeYeasNn+dAhEPqZRMMyJolI\nOyUGJEYAbspH1Qo1ERyFOjjlI2+UV6iZh7reZ/nQfaq7NJPlg7DPSabHwlI+zlPrcWJJyOUIwonQ\nwhBqz35B5MqEGjZgyyUKNUv6yGkmpjPM8nFxKNRL9d58KWHzyiacGkzhSN8Urllf+f5Zw8LgtUPD\n2LF/AKZl4/fv3jCnfew/Pg4AeMfaS/sz62WFiSM1Qr3YmBOhvvbaa+f8ho888ggOHDgAQRDw4IMP\nYuvWrfy5oaEhfO5zn4NhGNi4ceMFWaK8el0rjvRNYf/xMdy4eRn6htPoaIlhS8sGNITq8Vzfi1jb\nsAobm9fzbUzb12NuPwAAIABJREFUxJnUWXTGl6Ej1o6buq7DS+d24vXhvbix81o3Qq44h1qWAFOC\nbueRNwswbRNhKYSCpVWMzvOlfNilirBLkIr8usxDPQP5Gc9T/1+miiKwnKNQ53OOKlbUTr1a+GwH\nkgAYtFDP9dLKjpo/g0LtjCdrnh9Czb3egsQrELy53pqlQbBlyJJAJ0+2CN02YBLH/yz7yZnsWCxo\nwSEpmxXMzg/rlCjzAlT/BMNbiJgvLkrUkkiocSii7FOomd1GEWXaCKgCoWZ52bLot3xUk6M9G3gt\nH6mcgbrmhWk/7lW+YUsVVzxYykeJQh2h13iuYHKF+mKxfMzn3lzDwmDLqmb89NUzOHR6okaoFwkD\n4/S3auehYXzw5pVoqpudmmyYFg6dmkRbYwSdzdHzMcRFAytM7K8VJi46FjU4ddeuXejr68MTTzyB\nhx9+GA8//LDv+UcffRQPPPAAfvzjH0OSJAwODi7m8ACAx+H820sn8af/49f42+/vx7eePoqoEsUf\nbPkEJFHCP7/1fYzl3KKT/vQgDNvEqvoVAIA7l78LAgTsHt4PwNOO2i6yfCgiiEVTOlI6LbjqiC0D\nUFmhLng81AggsLxwrDhRghfAlSc/hBDeZn2mPGyAEriQpCKZcbszAvNQqEXZM0435QM2tcfM1CmR\nebpzRv68tAM2PcWFQbYLzdJAbLoSIYsCTWGxDNi2myHthbd9t12hMJF7pYnoT/mw/JOjQhkPNSEE\n01oSjaEGZPIGXjkw5oxX92RLS07qSGXLByG066YoeBu7VC6qnC34RMqWkM7pqAslkNGzFc/RbPcL\nW6rcehysKNF/m4w6CnU2b3AP9cVi+ajhwmNlRx1iYRmHTk3UWpIvEgbH6W+VZRP88vWzs97+SN8U\nNMPCVWtbLvkGSJ0tMUiigLO1LOpFx6IS6tdeew133HEHAGD16tVIJpPIZOiHbts29u7di9tvvx0A\n8NBDD6Gzs9Racb7RVBfGxhWN0HQby5clEAnJODVIye6Kul7ct+5DyJt5/OPBb/FiwlPJMwCAVfXL\nAdDuaW3RFvRnBkAI8WUT+ywfsgQ4RYWMxHbGKaGu1C0xX9QpEShWqClJUEoUaurXrWQvSOopvn0l\nUs+QM/OIyk7CBxA4nmrAyJsiuS2tfUWJtuwQoJkUakqoLWKdlzbl3EMtSZ5W7vQxy7Zom3mTtpiX\nZWqxoSkfNo28KybUjl+cjbkcXMuHCFkU+WSpmPR6FWovuc4YWZi2icZwA3YfHcWhE05snqn5fNGy\nKPJJUaBCXWRdcVugL6xC7VWS0zkDCTUBAoL0DF1EZwK/fpz6g0rXE0FwUSLrlpgtmJ6Uj4tDoa7h\nwkMUBWxa2YSJlFaSGFXDwsMmBAPjWXQ0R9FcF8LLBwaRys7u94fZPa66xO0eAF2J7mqJ4dxoBrZd\nm9AtJhaVUI+Pj6OxsZH/v6mpCWNjVCmbnJxELBbD3/zN3+AjH/kIHnvsscUcmg+f+7134O8/tx3/\n9f53Yl13PZJZnX9Bb+jchu1dN2IwO4yfn34WgEuoV9ev5PvoSXQhbxYwlp/wdc/zxeY5CjUAjDk2\ni44Y7a40k+VDIBIlYizlw9OZ0C3w8zeYkSSaQ23DLqv0MWI/0xgYckYeUSWCqbTz/gEWlGpgEQvE\nFiBLzMcr+EkxU6hn2K/XO30+fNSWbYHYlNRKkgRCXLLLEySciZPiiSm0iOmoy8EeaqByUoa3nbwk\nCaWZ4w7KpXxMO5FzDaE6TKc1XzqMu6Kh8EkAENzW3CxS2kW4mdULCZf4SkjndSQUmrpTzTVZCV6i\nblti2euJEOLEFAZZPhyFumBgOqNBVUSEa9X0NXiwaWUTAJr2UcP5xUSyAN2w0duewF3XLYdu2nhu\nT/VF6TYheOP4OBJRxdfU7VJGT3scumljeLI2oVtMXNASZO9yGCEEIyMjuP/++9HV1YVPf/rTePHF\nF3HbbbdV3EdjYxSyPLcfs9bWmYP3169sxoGTE0hpFlavoK//dOO9OPbMcew49wruvOJmnE71oSnS\ngPU9PXy5aGPHauwZeQNJYQK25CiAULGs3f3CNtZHgXN07FlQv1NvaztifVHk7XzZ8RlEhwSqiDFC\nLkcE/nrWqDESUn37aGyIcrLU2ByFWtTREQDeyriERRe0iufItm0UrALqI3FkCo7H17GgCLJd1fnl\nEKnHOB5TUV8XAcZFEMGCoLDuKRKIJcKwKUksu+9hd6KgxgW0Ni5scwUiUl9tPBaCadHGHxAJWlsT\nGM9R8m+bEmJRBbGYCmQlmESDItBEjPpE2Df2lpQG4niWG5rCqHe6AhYfX9JpAkSIgNaWOGIRGpEo\nSv7XkrMuCTZh8OeGLHpe2huaMXjO5mksgkogSPR7GI+F0NYa5x7qaFwpPc/O8dfV0eOQBAk2gEgs\n4LUVMNNriWg516oI3bDRlKDfGzlGZnddFUGZcLz+tgRiSzCJhabmaElHSJP54omIpsao7z07nKIf\nQaLqeXNdBG1tdbM6vhqWNjavpHnUh05N4M5tPRd4NEsbA47do7Mlhlu2duBnr57Gr/adw8fft6mq\n7U8PpZDM6rh5S0fZfPFLDb1tCbyKYZwdSfsigGs4v1hUQt3W1obx8XH+/9HRUbS20iWWxsZGdHZ2\nore3FwBwww034Pjx4zMS6qmpuc3AWlsTGBub2bTf7BQbHTo+hu4mt63o7665B19/43F89dffQFJL\n4+q2rRgfdz1LjQL1Yh8aOIFULgMQQBEV33sausE90P2TQwAAUpARk6NI5tNlx5fRcxBYJz5HEZ5M\npvjr03m67C/Yom8fuazGydLw6BTPrfbi1Mg5/vdkJlnxHDF7hUJUjE3lIMDxpgJI5XJVnV8GzdAB\nIkLXTBTyuhObpyGdpe9BbAm2RYsSbWJjYjxYqRxLTvO/B8bGEDMXVnEoGDpgizB0E5ZtA5KIgq5h\nbCyN4SxVoyyT9he0DKpmG7YBCAJAQijkdd95SacLPCljZCwJPSwEXpvj087/iYhUMg/bJEAIyGma\n77WTaapECxCQKmT4cwPOSpCgyxiZyPKJTzKTQV7XQWwRpmkhNZ3nHuqJ6TTGVP84dJMq7ew4BKeT\n4nQqW/XnXc13L6sVeFoKAGhZwTmOcbQJc2+sMJVy3teW+KRiYGQSEdlfxMSVbCIgky74xmvplGwP\nj6UxndGwpivse77aewt7bQ1LD42JELpb43i7fxq6YflqZ2pYWAyM0d/drpYYVEXCndf24scvnsTj\n/34QyxrC0AwLoijglq2diEdKRaQ3uN2jpeS5SxW8Y+JoBtdXN6+oYQGwqJaPm266Cc888wwA4K23\n3kJbWxvicfrBy7KMnp4enDlzhj+/cuXKcrtaNPS0ORWzo/4fyCua1mJb+1UYL1ASxQoS+XZOtN7Z\n9AAtSrRlhFX//EWRRU5AmeWjTo0jrsSQNXNlbRkFswDBojeGoNg83WRdB/03D7kKewEbhwABmRmW\n11nCR0SOYDqj0crqOXZKtJiVQBJ4Xrbts3xIfN/eVI2SMZley0e+7OvmCtdDLHD/M7M7eOMRVVXi\nMYUAYBIj2PIhegv7qrR8eFI+in3OzDddH6rzWT6YHz6uxjCd1nzWHMt2i+9kuXLKh00sx1fseKhx\nflI+NEuHYLskRLSoQj/TNTkTeCt7S+LfvSDbh+XxrJfmUNPv1fBkDoQsLf/0I488gnvvvRf33Xcf\n3nzzTd9zt99+Oz760Y/iE5/4BD7xiU9gZGRkxm0uZ2xZ1QTDtPHNp48iV5h7PcfOQ0Oc9NVQClaQ\n2NVKldh3XdWFaEjGr/b04/vPH8e/vXQKP9pxEn/z3b2YSBZKtt9/fByqLGKjY9NZCuhxVtFqSR+L\ni0VVqK+++mps2rQJ9913HwRBwEMPPYQnn3wSiUQCv/Vbv4UHH3wQX/jCF0AIwbp163iB4oVEW0ME\nqiKif7T0h/zDaz+AtyaOImfmsbqIUEfkCNoiLehPDyAqR4CihA8AtEDRUcmYd7lOTSCuxGETG3mz\ngJjij/AxbROGbUKx/Ap1YFFiUZtr5qFm+wnCeH4SsiChKdI4M6F2yKsshGBaBO1NEUykClRpnouH\n2iF1rNmJDdsl5h5C7fWLFyPr8U2fj+g8yzaph1oVAdiAJXDypZluwVso4m/jTUACixJ9pLsCKTU9\nKR+yJEKRJBBbKJkY5cwCREFEvVqHgYybksO8x3ElhulsyjfxMYlJibosFE26yhQl2gL1fsMtSjwv\nHmoPoSZODvlCeqgr+f3dVJXSz4wVJbIf8qWS8OFNYTp58iQefPBBPPHEE77XPP7444jFYrPa5nLF\nndf24ujZKbx+eATHz03jgbs3YOOK2ZG2gm7im784ikhIxn//05tKEmdqoJYPRRbRWk9XXCMhGf/l\no1dhKmfC0AyoioRDpybw/N5zeOS7e/G537sSXa1xEEJwpG8Kg+NZvGNNiy8w4FJHNCyjpT6MvpFM\nxTjWGhYWi+6h/s//+T/7/n/FFVfwv5cvX44f/OAHiz2kihBFAd2tcfQNp2FaNiVADhJqHA9s+hiO\nTB0LbPbSk+jC3tEDKJgFECuKkOy/GXoVaotYUEUFliFSAg6aA11MqHnbcZN1JiyNzdMtSvqUIjXU\nS5aCItEAmkHdHGlCXIljLDdRttU64EmUcMbS1hjF4TNTEMnMedHFYMqvLAq+Qr28WXCUSoGfK7rv\nYFXQq0qfV4VaEgCIIIbIi/dY7jNxOmLKsjtm+kRAbJ7oxuZVKkrkCjARuIrPukl6kTfziEhhROQw\nTGLBsAwoksIV6ogURTqrAxAhENGjUNNjoikfMxBqIkN2jkPiKR8L3CnR1kFs15Jk64xQz2+SpAcQ\n6kCF2iHUhLiTBwZWlMgKfi6WDOr5olwKE1tFXKhtLhfUx1T85cevwc9f68PPXj2Dr/7rG/jwrat8\nXfhmwolzSVg2QSZv4Fh/EhuWN/qfH0hiMmegKVpqZbgcYNsEQxM5dDRHfStJve0JXOOxXm1d3YzG\nuhB+tOMkHv3ePly/cRneODFOBSAA129qvyDjP5/obU9g37ExTGd0X5O6Gs4fan1Rq0BPWxynBlMY\nHM+it93vedzQvA4bmtcFbtdb1429oweoOmnKUIuSAFRF4oQYAOJKHF/4xm/QtdWNrSv+mjNCbZs0\nXs4qp1DbzvK9B9IMZCln5JE1c1hR3wtFlEFAkDVySKjBP46MsDL1sLku5BBEeW6E2pYcsuiqtjkj\nDxDnvPE26zpCZQi1V5WeL/kKgkksgKguMXbysgEgxRrhGCpCRZYPAIBdah/w5j5XtHwwgscsH5II\nWGLJNnmzgIgc5p7gvFWAIim8lT0xFLBSYJEo0CzNWR0QHcuHUHEVg2Yzq9y6whSzhVSoCSEwLAPE\ncq87Q6Pfk/laPlgjF2JJvKA3KF7Rsh27FRFp3rYHYZVmcJsWPZP1saXxYzU+Po5Nm1zDJUth8pLj\nhx56CAMDA7jmmmvwF3/xF1VtU4zzXUi+WKh2LP/7h7bi1nf24OFv7sJPXz2DD92+rupVjbO73LSK\nI/3T2P7OXv7/TE7HV3+wH7pp43duX4uP33VFiaXsQmAxP6PBsQwM08bq7obA9/U+dv/7N6N7WR3+\n5xNv4IV95xANy7jtmm5sf0cXtm1cdt7HutjX7oZVzdh3bAzJgol1q/z+8Evxe7QYmO9YaoS6Crg+\n6kwJoa6E3kQX/5tYUolCrcqib1k7KsUwUDCRy4hAPHh5m3lkLUNGXUzFVIbZDbyxeQZgSz41HfAr\n1EHkjXVIbIk0c0U0Y2TLE2pHkTV1egwN8RCdJNiVWzoHgSqfimv5sF2Fmp0jpvYWTA0hlCP5OcgC\nTW/ImTMr1AfGDuHQ+FHYxIYNG9ctuwZXNK0t+3qbMK+3Y/mwRViEHivr5EeMEEKK6LN80CdK1U7Z\nM3moKjbPsXxIogAYYmnrcTOPtmgrLzjNG3nUqQlORBkxBQCB0IkPs3HIogBRECBUWMVg3QPZhELC\nwivUhm3SSajlfje0vJOGM89JklG1Qu1ZESiaBAmCgGhYRiZPiXj9ElGoi1HclOTP/uzPcMstt6C+\nvh5//Md/zOthKm0ThPNdSL4YmO1YGsIy7tzWgx88fxxPvnAM779xRVXb7T86AlEQEFIl7HxzEB+6\naQVfvn9+Tz9004Yqi/jxr47jjWOj+Mxvb5p1l8CFxGJ/RgeP0WLr5kSo5H2DxrJleSMe/Pg1yOQN\nbFjeCMX5TT7fY74Q126zU9tx8NgoVrS6Nq1L+Xt0PlHtWCqR7gs/nb0E4CXUs9rOQ6hhlXqoVVni\nRYUAEBKovcNylreDmrsULEqoTUNEIqpQMkbEkk6JhARZPip7qFlBYmukGXEn97digxlHodYKThRf\nIsQnCbMtSrSJxQmnLIkgTvJFwXIJ9UxdGJnvvDlCI6uqyaH+0bGfYufQLvxmeA92De/Dtw8/wduL\nB4F5vak1RfQp1GkfoWYear/lQy7yQEpOsx267/Ieam+RnOTYYggRfNtYtgXN0rnlA6AKNUAnRlE5\nglTGPTbBlrlCzSwfgiDwlvXF1wghhLZIt0U+MeCvrTD22YJ9vrYpos75UcjlCWRRnr9CXbWH2qNQ\nB0RpMR81sHSKEiulMAHAPffcg+bmZsiyjO3bt+PYsWMzblODi5u3dCCkStixf4BGbs6Agm7izHAa\nKzsSeMeaZkymNJwZdu4xhODlA4OQRAFf//y7sO2KNpw4l8RD/7wLo9MLb3W7WOGNzKsWqzrrsHV1\nMyfTSxXepI8aFgdL+4paIHS3Vkeov/fsMfzTU4f5/yNyBK0OuSO2XFL0oCp+wqWAqoqVlrddD7XC\nyYZI/BYL06ZFZsUKtTdRIohQuwp1E+KOKl2JwDAFWMt7CLXTrEa3jarbRBNCYIE1TPF7qAGUEOqC\nGUyo82YBBAStEVr4k51BobaJjaSeQm+iC1+64Qu4rfsmJPUUXh3aFfh6y7Zosw9HoZZZR0dighDC\nLR/EVJ3GLq59gh5nOQ91FQq1Q9oFIkIQBK5se1caGHmOKBGXUDvXS0bP0oQPbwcxW3IVak9yhyQE\n2zhsD8lkEwO2zUIq1LxBji2hyfH+ZXIGYnKUW1fmCt0yaFMkCFwBNyqlfNhC4DJ6zBO/1bBEihIr\npTCl02l86lOfgq7Tc7V7926sXbu24jY1+BEJybh5Swem0hrvzFcJJwaof3pdbwOuXkcnKfscRfb0\nUBrnxrK4am0LOlvi+MwHN+FDt6xEtmDi9beGz+txXExgkXndtazlEjQmQoiFZfSP1Aj1YqFGqKtA\nJCSjtSGM/tFM2SVNy7bx8puDePXQMM6NuRdwb6LbeUE5hdp9THaKsNjydiVCDUtGNCRDlgSuNDKw\n1AZZLvbrihXJ2zhXqFt4MWTGKP9lZCkfWScjuCEe8h2TXkHp9cJL1CTJb/kA3OY17n6DCTWzAyTU\nBEKSOqNCndazsImN5nATWiJNuGvFu6GKCp49syNQpbZIsUos8oJCm9hI62kIEAFTgapIXMF2DzTA\n8uF5TTWxeaxAVJIEx27iIdSGQ6jlMMIeQm0TG1kzRxM+0p6VA1viXRy9qrMI1tbcfw7MouMHAFkI\n7tg4H+i2qyLXxahfPZ03EFdj805u0WzdIdRwFWq7UlGiCCmgQp5F54mCgPgSKQjzpjB95Stf4SlM\nzz33HBKJBLZv387j8ZqamnDXXXcFblNDebz7Gvp78HwVnfzePksz9a/obcTmlVRRZYT65QMDAIDt\nV9JieEEQ8K6ruyEAeOvM1HkY+cWJwfEsQoqEpvoLZ3O5WCEIAnrbExidziNXWNhY0xqCUfNQV4me\ntsoVs6NTeRgmJYavvDmE+95Nfbgs6YO1o/ZCKVKoBSdrV8tRZ2pFhdqiudaqTD3LhWLLhx2tmPIR\ntEQ/lp+AAAHNkSZMFejNPKOXJzCsKDGdAkKqhEiItla3LZpOrNs6wphZvSuOKPMq6QBgW87fHg91\n4HgcshVVIojK0Rk91EktCQBoCNHmLwk1ju3dN+L5sy9h59Bu3Np9o+/1fh+zW5QIULKZ0jMIi1Hk\nICCkSM7EoDjlo3TVgFThoWbniHmWpUCFmh5vVHYV6oJZ4KQ6rsSRzLrnjtmNWIttltwhizL0gPHw\npBHPxIClfJjWwinUbMJELAlqiFqb0jkdnXIUA+YQLNsq6Ww4m32zpkhuDnXQ5IlN8kpbjwOu5aMu\nppQULV7KqJTC9MlPfhKf/OQnZ9ymhvJY1hTF5lVNOHRqEn3DaSxfVt6PefTsFERBwJqueoRUCZtX\nNmH/8XGcGU7h9cOjaK4L+7KT4xEFy5clcHIgiYJulvQ9WGqwbNpau6ctvqS+gwuJ3vY4jvRN4dxY\nBut6Gi70cJY8agp1lZjJR+19fOehYe6RW9+4BgIEkEKMWjw8CBV5qG2Dkk+94Fg+gjzUHoU6rErU\nYmG7MXWsuI4Vr3khiyInb0GK4nh+Eg2heiiijJhajUJNCVwyTdDoLHsrsgTbdNM4qoGXqHLll7g3\nSK7iz+ChzjoEPyZHEVOiMyrUSZ12FawPuW2j7+i9larUfTtKzpE/aUPkKjE7hpSeRsix7YRUmlhC\nfEWJAZYPyY3Nq9QchZFblvvMvNd2gEJt6iImpujjOTPPJ2ZxJYrpDD13dVEFtlk0NpbcwUhyueP3\nTAxkkanZC++hhi1BkUUkoirSOQMxlS7rzkel9uVbV/JQez3rUnmFeqlkUNeweLjjGtqK/IW958q+\npqCbODOUxoqOBCIh+h1jto9/euoINMPCLVs7SojkppVNsGzC1e2ljNGpPEyLoKulZjEqhxXL6G/b\nL37TV5Vvv4b5oUaoqwQj1MfPBd+oGKHubYsjkzdw4AT1yPXWdeMT3X8Ea7xrRoXaKjhLx7YEWZAD\nyaxfoZagKtRiYdomLNtyiU2Ah1ouIoBeGLaJaS2JFsd/nFCq81CrooJszuKqvdcXrgcspQfBS1RZ\ngoXPe1yS8hG8X0ago0oUUSWKgqVV9PZOOwq1l1An1Dhu6b4B01oSu4f3+cfpJf5OZjOboGSNLAzb\ngAI6EQkpTlHoDCkf3lSNStFzruWDngNWYGrD5jYkloP91ok0fvYy/bEumAU+MYurcUxnNIQUaqVg\nEx9+TB6FGii1fBQXRgLgSnEldX220HyEWkIiqqCgW4hILJ997j5q3dLdbPBKHuoKjV0A2jgBABqW\nSEFiDYuHzaua0N4YwW8Oj+D0UCrwNcw/vb7XVRWvXNMCURAwMJ6FIAA3b+0o2W6T0zjmrTOT52fw\nFxEGxmZfkHi54Zr1rdi0sglvnpzAP//8COwqUnhqmDtqhLpKbFjeiLqoguf29GMyVdq+lBHqex2r\nx6/fHOLPySQGQCjxUIuCABmu/1JnhBoConIUmQCFNe9TqGWosgjCFGFLc0mQo+554VV+iwnQRH7S\nKeijRZSxKlI+ckYeIYfksMIsr4e62qSPkgSLIssHighQuU6JTLmMKVHEnNi4SraPpEZ/zBo8hBoA\nbuzYBgA4MX3a97hZNFnxFk9OOhYZmVCrRUiRaA448U4MSi0fgiBAEIInOV6UWj689h2mRtNrQy+I\nMJwow7xZ4JOimKNQ18dp0aTlJdQeD7UsKM54iiwfHpLJJmuMfFca+2zBcqGJLUF1FGoAUEDP7Vy7\nJdrEpvsumqAFe6g9lo8gD3WEKdQ1Ql3D7CAKAt57/XKYlo0vf2sP/tv39+HAiXEf2fH6pxniEYUT\n7C2rmgPj8VZ31UNVRBy+DHzUA0Utx2sohSyJ+JMPbcHqrjr85vAIvvfcsaqiLWuYG2qEukpEQjJ+\n57Y10A0bP9xxouT5c2MZ1MdVbFjeiJUdCRw8NYEppwBMNygRCSmlp1uVZa5iajmX4ITFKDJ6qULN\nYvOIpXCFmlssLJ0TG+L1+TrwJ0r4CdBYnirqLQ6hViUFqqRWJC85Mw9VoETar1Cz7o3VWj48aqAT\nm+e1fMAWubWF7je42JEr1HIEUSXieywI0xqzfNT7Hm+LtkIVFZzztO4uGScj/s75nHLUblZYqioS\n9STP0CkRcIsAq8mhZoqwl1Azq0jBmTwYutswiBJqeh3FZNolkeWFc2864EQBMpJcxvIRYINQzgOh\n5teN5Vg+HPIq2fQam2sWNVu9sauwEFlFNqRiMA/1UmnqUsPi4patHfjc712JTSsacfTsNP7nj9/E\nw9/ei6EJer99++w09097ccMm2oCEFTcWQ5FFrO9pxOB4NlD4WUrghLqmUFdESJXw2d+9Et2tMezY\nN4Cv/WAfdh4aQt9wGrphwbRsaIaFvGYimdEwNJHFycEkjp+bdhtc1VAVlnbVwgLjxi3L8NIbA9h1\nZBS3vmOKt4HNFgxMpjRsXkWX227Z2onTQ29j56EhvO+GFdBMSkSKFWr6mAidyAhJArI5d+YYEiPQ\nbQO6pUOVqApmExt9qXOQIAOmgpBKFTzbkiCCKsIsFzjY8uFaFIqLEoeyIwCA9lgbfyyhxHjL6mLY\nxEbBLCCh0mPmhFoWPQp1dYSaZyl7vcleZdeSEQ8rmDYrK9SsSDKmRBGVqfWiUnQeU6jrVb9CLQoi\nuuKd6Ev389bdgOeccZLlKtBTBaoIiU5haUiRYFp2qYc6wI8rERUEbo51ELhC7bF8FCe2MIXa0ETu\nzS+YBWSdwlLBCoMgjYa4Ck23AE+Tl2pIMleobcFjD5m5oHK2KPZQhx0PqWDR78Fcs6h5sSPzjldR\nlBjUehwAVixLQJYErO2pL3muhhpmgiAI2LyqGZtXNePsSBo/f60Pu4+O4kvf3I0PbV+F00MpLF/m\n+qcZbtqyDJtWNlVsJb1pRSMOnprA4TNTgbaQSwnffuZtvH12Cs11YTTXh5GIKphIahiZyuHsSBqR\nkFRrq10FYmEFf3HvO/Do9/bhxb3n8GKV2/3+e6/ALU6STA0zo0aoZwFREPCxO9fhy/+yB9977hj+\n6ve3QZZyoUlnAAAgAElEQVREnHPsHj1OXvW1G9rw7WfexuEzU3jfDSug60yhDiDUsgRdi6OnuQFn\n8u4PO1vezhhZNDmE+vDE25goTKJX3oi3ieh6qHWXwCqiQ8oDLR/lFepzaarGdsfdL09MiWEoOwxC\nCO/OxVAwNRAQSDYdWywi8+OZqXiwGGaA5cNHRG0R0bCM6VRlos4sH1GnKBGYSaFOIiyFEZZLb8g9\niU6cTvVhMDuM5XW0iIhZUwifrNhcSWepKDBdQq0Zlt+6UsaPK+lxmABGcmNlx8oVaucrS9NBHPuO\nQ/SZh1rXJMCkr/MWJdoGnRg0xEOYSmu+yEZ47CiyFEw0vasfnFBLEggRFrT1uO7JoVZkkdsriDP+\nuSrUvHDXouO3KsQweiMSgywfXa1x/OPn3zWncdRQgxe97Qn80T2bse3oKL71y6N44ld0BfSK3tJU\nBkEQZiSQLPnj8JnJS5pQp7I6Xtw/AEEAhib833lJFNDWGMFNWzpKfptqCEZ9PIQvPXAtpvIm3jox\nhoHxLEYmcyAEvCZIlkVEnUncywcGcWIgWSPUs0CNUM8SK5bVYfs7OvHSG4N4/fAIbtrSwf3TrHAx\nGlZQH1cx5nSs0owKhFoRQU5ch8/cdSP+5Lmd/HG2vJ0xsmgKUyX8pQH6fCc24m1kqIdakYA8UyM1\n/omSAIVaFAQICF7OP5cZQlgKozns8eypMRhpE5qll5BO5k2WCCXULKKJth6fnYeaEUKmBkqedtz0\nWCTEwgrItD89ZOfgLrwy8Dr+/Oo/9OVOxxTX8lGJfCX1lK8g0QvW5fJcepAT6uI0EgKX+DMPNWGE\nWpUga/6iU+qhDlKoozBtaQZCTa8hZsfw5Vc7z+U9CjUgQhEUWpToEGpTo9vWx1VkCwa35vBjYgq1\nJIHoIUxqfh+mL4ebJYKINIt7IRu76B7LhypLSEToNWbq5RsezWa/xJIRiyhI5eiYK6V8sEY6NdRw\nvvHOK9qwprse3/zFURw6PYGr1s2t42RXSwz1cRWHz0zCJuSSjZRjhZUfvnU1br+6CxMpDemsjqb6\nMJrrQiX1KDXMDFWRsLmzAe11lSdllm1j56FhnK01hZkValfkHPC+65dDALBjPw3XZ4S6u82N72lv\niGAiVYBp2dCdfOri2DyAxswZBm2zbBPCCZfICLVTFDiaG8eRiWNYVb8cikFJb1iVEJJFH4E1PKSv\n2EMNuEVt3iV63dIxmhtDV5zO9pNZHa8dGkZcdmLKAggMy3wWHIU6otL9UsvHHD3UPsuH10MtUc9q\nUVHi7uH96Ev342yq3xlnHqIg4umdAxgbN51xBls+DMtA1siVFCQydCforLzf46P2NjaRJZF3KwTA\nyaet0/MRUuj5J8UpHwE/ArIoQtDjGMuPl+0uyS0fzEMdsNrAjpVYVMlVxJCvKFFzJl7FDXjY2HgO\ntSTCzscwrSWLOnCWJl8wL/eCeqhtj0Kt0BxqwO0gOleFWuf7Fen1ROh1FpRGwywfQu0WWcMioiEe\nwmd/dyu+/ufbS/zT1UIQBGxc3oRUzuCrp5ciDp6ijcY2r2xCWJXR1RLDFcsb0dYQqZHp8wxJFNHd\nGsPAeGbGuL2hiSz6hsvbFS8n1K7KOaClIYItq5txajCFvuE0zo1lIEsCljVF+WtaGyMgBBhPFqhf\nFcEKdUgRYVoE6RxdXm9mHZ9Mv1/01wOvgYBge9eNXPH2xuYB/qLEIA814MkY9nioB7PDICCcRD79\nmz48/tRhELO8Z5X5leGQtyCFulrLhzflQ5YcYuclopZEY8o8ucGEEAxkaZLKQJa22s2ZOUTlCH62\nsw8Hj6edcQaTr6TjVy6nUHfElkEURPSnB/hj3nMrSYKP1E4VaFGipakQBEpKS2PzynioJREoxGHY\npmsdKQKbKDGPvByQ8lEwCxAg8IJERVCRt2hsnizKyGTojbEhpvqKRwFHPWfJHRLNTQfoRI7B8k18\nXPJd3GBmvuBWE1uCIrmEmrW4n2vKh9ebTXOkBYiQy3ioWe537RZZw+JCEAQeyzhXbFpJRZdLNe3D\nJgSHTk2iPq7yld8aFhe97QmYFimx23iRyun4m+/uw6Pf24dMvrrOyEsZtV+LOeK2q6gl4Ff7zmFg\nLIvO5piPwLY1UMvB6FQeeoWiREWmj006iSBsO0Zmnz/7El4b2oPfDO1BQo3jqrYtKOiMUMtFqRr+\n2LxAQh2QGzyQpsSU+aeZVUWsUATGlV9GqEOSczyzL0osbmldzvIB0MxmzdSR0tNcqRzM0PFnjRzC\nTowfVzPLKNTTRV0Si6GIMjpi7RjIDHHVmJ0z4ii0XtuFYRuQBQmGJiKkSBAE1qCmcmMXwCHHDoEd\nLmP7MC1KqBWJKdRuEorFixLzUEUVgEN2ofKUj7gSQzJLP4+GRJBC7SfJtjOekdyoOwZvUSazfDir\nCQtLqD2WD0XisXnZHJ0QBsVJVgM3PUTmhEUgckUPtVi7RdZwCWLjJZ5H3TecRiZvYMvK5prl6gJh\neTudyJwdKa8+P/HCcWTyBjTDwssHBsu+7nJB7ddijti6qhnNdWG8enAYumn77B4AVagBSk41gxKy\nch5qADziiG2n5FuxufkKDGaG8d0jP0TOzOOmzusgizIKGiU2YVVyiJHjobY0vDq4CwBga5GSokQA\nEAM81CwerjtOC1gmnLGIxG878SLvKNS2ITtjYQq1OAeFmhE1SkIlyS24o28icQIkEhmapWMg4+Z8\nD2Ro4WTOzCMsOoS6QI+9rEJdJuHDi55EFwzb4N5my6dQi77CQABIqAnohs0/Z8Vjx6HbBVs+JFHg\nBHa0HKFmHmrBU5TILB+8KLEAVXS9cTJRYRMb01oKcSXGuyTWx0K+zwlACaEmnFC747E8EwpmJ2Lj\nWEgPtVZUlBgNyxAFgXZLVKLzVqgJsxABEIgUOPGz7ZpCXcOli4Z4CMuaojg1mLwkm3lwu8eqphle\nWcP5Qk97AgDK+qgPnZrAa2+NoLctjpAq4YW95y77boy1X4s5QhQF3PqOTn6zKl6Wamug9o/RqTzP\noQ7yUKsO6WUKdbujUOuahD+68gH81Q3/Bb/Vexs2Nq3Hbd03AQBXqEMKbT3OvMWvD+3FwfHDqLM7\nYE+1ByrUQY04zmUGIQoiOmLtdCwpp5jQKbBLB3RsZAq1xQk181BLrtd5lgo1K9oLauzCWj0LRIZm\nahh0bB4AtazkzQJsYkMVqGVGK0i+cRYjyRXqCoQ6TlchmO3D8Hqoi3KoAdplUTMsTqiLPdSkguXD\nzNHrpVxhIvu8WAKHVx03eVFi3keoRVBl1yKWQ6g1qIqISIgqv/C0vfdahGRJAMmXEny+kuCxfLDP\nqlihtomNZ8/swHh+9goZ9zQ7hFoUBMSjCtI5A3ElNmeF2mslibLryZYCPdQmt3yUToJrqOFSwMqO\nBPKahZHJuX1fLiQOnZqEINBW6jVcGPS0xiEgWKEu6Ca+9cu3IQoCHnjfBtyypQNTaQ17jo6W7ugy\nQo1QzwO3bO3gxKJYoW5rZJaP3AwpH/SxKUcVbq4PQxQE5BwVuiXSjHvW3I0/fsenkFDpexQMC6oi\nQhRp90XW8GQwOwxVVLDcuAmAEFyUKPgJtU1sDGSG0B5thSIp0HSLe6EqxZQxomrqEkRB4BMD2oBl\ntkWJ/vQMQRC4kg4AxHIVRRAJmmVwhXp5oge6peNsmrbaVpxGM4UCgSRIZRXqaZ01dSlPqHlhokOo\ni5t9yEWWjjo1Ds2w+WdKs6JnbuwiSQKsPL1eyhFqwzZBbLf5SnGTHpoLrkGBS6gF2+3CGVdjSGZ0\nNMRCEAQBIc91A/jzliVJBNEjkAUZI1n3BunvaCny1wZZPg5PvI3/OPU0fnnmhcDjqQSv15ldVwmH\nUMeUKPJmfk6KOC92tCTayVISALuMh9ppaFCzfNRwqWLFMnpvO3OJFYxlCwZODiaxurOeCyk1LD5C\nqoRlzVGcHc2UdFf891+fxkSqgPde34ve9gTu2NYDAcCzu/sv606MtV+LeaA+HsL1G9sRUiUsd5ZH\nGOIRBdGQjNHpPHTDhigEZxArRQp1PKIgGpaRL5RPTSjoFrdYhIqUxg+uuRuyRYm3EqhQs6JESkjG\n85PQLJ37pyfTbnctW6c3s6COjYyoGpqEsCpxn5tfoZ5t63GBe3N9RMamiiojQJqpYTAzDEVUcGXr\nJgDA8elT9Pgcm0pBtxFVIjybuhhu2/HylfTd8Q4IEHhGt+nJoWaWDz+hTkA3LIRUD+mtIodaFgUQ\nS0ZjqL6y5aPEu+zE5hELmuXkgsNthS14rouYHEUqq6PBaZWtyqJfofaQfXrdCGhUmzCSH+c3SD5B\n8ijtkiiA6GHoROO+dAA4Nn0SAHCyqH17NdAtHSKRAAj8+1EXVZHXTN6wp1JL+Ur7BcCJuuJkplvE\nKiHoNQ91DZc6VnY4hHro0iLUh89MgZCa3eNiQE9bHHnNxHjS5QX9oxk8t6cf7Y0RfODGFQBo7ddV\n61pxZjiN4+eSZfa29FH7tZgn7r/rCjz6hzcgHimdSbc2RjA2XUBBpyQrqLiCqdbMQx2PKIiEJK5Q\nB6Ggmx6LhQhiqhAgYm3DKmzvuoH7mOQAD7VcpFBz/7Sjxk542tUamkOoKyjUel7iBYmAY2txiF7V\njV2CrASepXbCCRBNzdBMHcPZEXTE2vm4j09RAicSl1BGpIibRlKEpJaCAAF1aiLweQAIy2G0RpvR\nnxkEIaSoKI+Sf6+lI6bEYdmEf6aCINAJjDNhFyEFXgOMJLdGWjGtJVEwSlsGWw6hZtF2kiS6jV1s\nCzlnG8lz/Cw+DwBkhEFACxIBZ2WECG4snO1Vnel+G5Qm6JbOifLpVB/dbz7Os20lUYCdou3qjzmf\nAQCccIj0aH4cSW12P+iaZUCEE/3nFO2y75cqUCV/LlnUvg6MilixgNaqWT5quMTR0x6HIACnh1MX\neiizwsGT1D+9ZVXzBR5JDcu5j9q9h/9q3zkQAtx7+1pf0MKd22i/hmd39y/uIC8i1Aj1PKHIIupj\nauBzbQ0RmJaN0elcYMIH2x4AprwKdUhBbiaF2tmfqkiAqeK26H34P658AKIgwnByr4M91P6iRGad\n6HIKErl/GoBWECAKIjJBHmqHqBYKAlfLAUehhgCJKNW3HufFbm52ttfywby0ipNMQUBgEgud8WV8\n3GecLGrRdj+LsBRBzswHLkFNa0nE1RhPPSmHnngX8mYek4UpXw6zHNAiPSpR37HX2kNtIQ7BLvN1\nY5OIlnALAGAwXepDM4nJ4/oA+KIFTdvkXRJF4pJoYrqfi2RTbzlLzKB+fgGSQ1y9ueXsuqmXqUI0\nkhsDIQRHJ49DtMIQNVfVlyURlkOo356kHd4KZsEXN3gyOTuV2lWo3RoDVpSqOpaeuWRR+4odJZFO\nRp1zWOyjLs79rqGGSw0hRUJXSwxnR9LcwnSxgxCCg6cnEI8oWL6svNhRw+Kgx0n66HMKEzXDwuuH\nR9CYCGHrav+EZ213PVYsS2D/sTE8u7sfrx8ewVunJy+rOL0aoT6PYD5q3bARkoN/mFXn8axDoGOO\n5UMzrMCKWZsQ6LrlKtROoWPIboDqtCg3LUogK1o+mEJd1HJ80qNQ5zULMTlaNjZPEWVomluQ6B0P\nzfelJKUv1V82XxkoimNzVFKfMmhLUGTJIUDu413xDtSrdYjKEU6ABMsl1KoQpt7iIusJIQRJLYWG\nCgkfDL113QCAI5PHSnKoaRa0qziHxahzDvyEWnDIoYTga6CUUA+XvMa0LSeuz+tddnOoWZdEeI7f\n9hFqSkRZW1l23Um8taa7b0as6zyEejA7jJSehpJv44o6GzvJJRASwnh76gQIITiV7INNbKxpWAkA\nOOHYcaqFbusQiJOl7RDqiDNuGXRiMC+F2onjU2QRthmcSGPXPNQ1LAGs6KiDbtgYGr80ChPPjWWR\nzOjYvKrpku3wuJTQ6yjU/Y5CvefoKAq6hZu2dEAssi8KgoC7rusFAfCvLxzHN376Fh574g3834//\nhguGSx21X4vziFYnsQMIzqCmj7sfQSREs6MZ6ckH2D50wwIBEC4iRixJBIDH8hHg15UkEFtEzswj\npadxLjOIejXBCx69lo9swURcjQXG5uXMPCJyBJZNeJdEwCVAApGRMbJ4/OC38d/2fB1f2/cPPsWa\nEOKJo/N6qJmVgO2HqsCKzJbo3fPVGVsGQRC4Sg34bQ5MzcwZOeiWgQNjh2ATG3mzAN02UF/BP81w\nTduVECDg1cFd/tbbLC/bY/kICZRQhzyfqSK7xLecfYApws0hOuMfTI+UvMYils+7LHvU8aSW4go1\nPNnSluH+LTBCHfZ47+Eq2sSrfjvjSUi0OcRIbgxHJo/R5/Jt3HYCMHuIgDalB1PaNMby49zP/u6e\n7VBEmds/yqFgFvDQzkfxg7efBEDJrVCkULNrjE0M5hKdp3sy2tn1ZDvXk1ZUmMgV6prlo4ZLGCsd\nlfdSsH0QQvCTl+m948rVLRd4NDUAtHalMRHCWafj5itv0hXtm7d2BL5+2xVt+MLHrsZnPrgJH79z\nHbZf2YF0zsA3f3HksihWnF87pjngkUcewYEDByAIAh588EFs3bqVP3f77bdj2bJlkJxosK9+9ato\nb29f7CEuGNobXUIdCojMA1xCDIBXNEcc0pPTTL5Ez+A2dZGc/TJC7arZhmVDFFxS6oUk0U56Q9kR\n/OUrXwYAbGxez59nlo94REGuYGBFuAlD2RG8PrQX13Vcw1+XN/KIKXFnLB7LB/MPExk5M403xg6h\nTk1gsjCFp08/j3vW3A0A+LfjP8OOc6/gntV3cw+1QESuSsiQoQHcS8s81LYpgdE5RqQ74x2cxNFC\nSs3Zh0O+zByePvMCXhvajXtW343NLRsAVE74YGgMN2BT8xU4NHEEYYnuj3i83l7Lh0IivnMA+NM4\nypEzRmSbVIdQp0YAz2VPCIFpG4At+1I+rFQTRFvBL8+8gO3dN9DXeiYUpu55P5M+zhVqtpJAZNoH\nJsDyERMaANDoPFYsKWZaixRqx/8tdaMfx3F08gROTJ+CKIhY17gaK+p6cWL6NHJGDlHF7STqxSuD\nr2O8MImdg7twZ+9tMGwTkh2sUIuMUOuzV9y8OdSqLEKVJVimCBmlCnWtU2INSwErPIWJt2yd4cUX\nGL/aN4A3Tozjit4GbLui7UIPpwYHPW1xvHlyAicHkni7fxpX9DbwBnTFEAQB63oa+P8JIZhMaTh0\nehIv7h/Au67uXqxhXxAs6q/Frl270NfXhyeeeAIPP/wwHn744ZLXPP744/jOd76D73znO5c0mQZm\nr1CzwqtKCnVxG3O2PevGCACmaUMuYzGRRRH6iStxa+fNeEfrZqyo68XNndfx5ydSBdTFVNTHVOQK\nJj605n2IyGF8/+iPcSpJi9JsYiNn5hFyMo99lg8Wn2c0IqHGcf+Ge/FXN/xfaA434oX+lzGYGcbO\nwd3Yce4VAMBTp55BvxN55/VNM+8qUyoVDwECaOYzU9W74sv4dl5VVnISPw6NH8FrQ7sBAL888wLO\npuj7Vcqg9uLmLnp+WHKFJDidEEV/zjQj1F4PteLx6ZZTqBk5j8kJKKJSolDvGXkDBbsAUoj5FXwj\njNbU9TBsAy+cfRmA22hHEADDQ6iJTidmTKH2riTQF3gmM8zHThTUqwkMZIZwYvoUOmPLQIyQL6mE\n/d0k0RvloYkj6EudQ0+8C2E5jDUNK0FAcDJ5JvDYDcvAr9jYiY1n+3bQJ2w6cWLknhFqmMHdOwcy\nQ3hs79/j8MTbge8DeDslSp4VD2dCWuShZn75moe6hksZ3a1xSKKAM+dZodYNC//fU4fxTz89NONr\nCSE4O5L2WRrPjqTxxK9OIB5R8Acf2FRiJ6jhwoHZPr7//HEAwC1Xdla9rSAI+P27NyAWlvHEr05g\n+BLMRJ8NFpVQv/baa7jjjjsAAKtXr0YymUQmE9yFZymgIRHihCAogxrwK9TxqEOomUIdUJjobTvu\n3b7Y8hHUJRGgZMlON+OunrvwB1vux+ff+Se4snUzAOrPnkxpaK4LIRKWkdNMtEVb8anNH4cNgn98\n81sYz0/wiDbWRKW0KBFonN6GR276r7iu4xqEJBW/t+4e2MTGP731Pfzr208iJkfxu2s/CJNYODh+\nBICfcDI1V/R4ab0EqCvmLjl1ev42dXcsLPHjaScL+caObShYGn5y4ucAqlOoAWBj03pfR0U2NkEQ\n+N+yKPMYupBPoXazqKUyaie7RmwbaIu2YCg9ytudZ4wsfnz8p5AFGUb/ejcFxSG9oXwX3t27ne+L\nNdqpi6kwNPdHyXDOS4Qr1M4YnbGJcFNo2HhMy0ZbtBUpPQ3DNnFF01pYNvETamccYbsOjaEGvDVx\nFBaxuH96TcMqAMDJ6TOBx/6b4b1I6mnc1n0T6tQEfjO0BwDNilZkd0xskknM0mz0pJbGPxz4Jk4l\nz+C7R37o+smLoHti/xRZ8hPqMgq1VHPF1XAJQ5FFdLfF0T+a8RHYfcfG8Myus3j77FSgcDMbGKaF\nv3vyIHYeGsa/v3QSbxwfr/j61w+P4K++uRtf+MZreGbXWSQzGr7x07dgWjYeeN8GNCZCFbevYXHR\n6/TYOD2UQiQk45p1rbPavjERwifesx66aePxnx2+ZApk54JF/bUYHx9HY2Mj/39TUxPGxvy5uw89\n9BA+8pGP4Ktf/eol77kRBQGtDZR0BnVJBAAlQKFmpCeYULttx7379Vs+SAVC7WQX26XnNp0zYFo2\nmurCiIVkEAIUNAsbmtbhw2s/gLSR+f/ZO+/ANuq7/79POg1reMvbjrMcZ+9AyChQoKzSAgXC5umk\nQKH0SQtPygPtQ0mhUEoLtLQF+qMpLWGkbVooYYbpBLITZ9pJvIfkJWtLd/f743Snk3SS5Ti2PD4v\n/sCSTqfP9+5y977Pvb+fDx6oeQQ/rXlUjD3sUVaWzdNoxIYywZAQ9bh8Tv5MLLDNRbu7AwIEfH3O\n9Ti7fAXOLF4iL6ONEtSRRi5ARFBL9okSRVa62FwIJvxfwB/5TSZsf+AFHmcVL8O11Vei1FIsd35M\nVoNaiVajxfKSpZExKjLp0hitOgu84ZsdOZsKQMdGsthajbrDShKoHCeg0GSDnwugIVy15O/HXocr\n6MaS7JUQ/KZInW6GgYZhwPECvjLlIkzNqhTX4deDgeh984fbr5tZE/wB8fiQPdTSjVzYWhF1MxMW\nySFOQKE58uh1Zm4VQpwQZfmQbwYEATNyp8nvT88RhXRlZgU0jEZ1YiLHc3inYStYDYsLJp2DL5Sd\npeiaqY06hqU5A1z4xkDKUAe5IP64/wX0+HtRbi1FX6Afrx9/S3U7B7hAuKoJE7Z8RG52YivSSBlq\nDWWoiTHO5OJMhDgBzXbxvNfY0Y+n/74fG9+rwyN/3Y07fvUh7n9uu9zuezCEOB6//fsBHDjRjary\nbGg1DF58+6j8JFWN2pNi91SnO4CN79XhB09/grYuD85bUoYF08g7PdqoUFRbOXNWYcKn7clYNrMQ\nZ84uxIk2Jz7c03o6wxtVjLiHWkmsYL7zzjuxatUqZGVl4fbbb8eWLVtw4YUXJl1HTo4pob1hIGy2\n4S/LU1ZoRVuXB1lWo+rvFboik6FsuWbYbFYU2cQ7Qq2ejfvOCbsoJPJyTLDZrLCGBTY0jLysIAhg\ntRrV3zOFPdmZWRmw5ZmjPuvx9oRjzoTTLfqQM8wG2HJN+Fr+l2C1GLGzdT+anW1gggzKrOXYDUGO\nRcKgZ8EL8dv31jOvw6+3PY+zK8/EqimLxPeyrsOxLfXo8vSA1ejk72ToxTilDHVJURasZgMEpyiS\nZxZPUazfitLMIniDPvC8ouoGK47PqjfjG2dcBavBgm8tvRY/ef9xAEBlYTFsOakdA5eazsGWk+9B\ngAAdG9kvLMNCAJBnzoZGJ8ZaVGCNjMOoA8+JEtyg06nuE4tFvDGxZmVgtnY6dnXuw2M7n8aM/Kk4\n4qhHZXYZVpSuwvv4HNmZkeOIZTWAhkFRYTbu/+JdqO8+iT/8pQ1GgweZFgOa7QyMADIzLOCD4Y6e\nxdmw5ZvBh2+oJMsHq4mMKS9HnNFtzNBhqq0MH7eIn585bR5+L7wDg14bWbZHzAYbjHosmzQX29p2\ngAGDM6bOhVlvAmDFlJwKnOhpRGaOAQZW3K82mxUfnhS90+dPXYVpZaUosGVhS8N7YudCXguD4vjv\n84sXaFZrhJbTwCd40cPY8Y9jW3DC2YjVk87At5dejx+++TNsbfkEF85ajSKLDVvqPsCxrhOw6s3o\nDvTKVU0KC6zidneI546OYDv+1fQG6rsbUGixod0r2m5MBsMpnSdG4txCEKlQGRZEJ9v6UVFoxYa3\njkAQgMtXT4HXF8LJdieONffhVy/vxZeWlePKL0xVLbkaS4jj8cw/a7G3vguzK3Nw59fm4e1drXj1\nvWP416cn8bWzp6p+73irE0a9Fg/fuhwf7mnFuzubkZ9lxFVnT1NdnkgvtiwjMgxaeP1cwsmIqXDN\nOdOw/WAHPt7fPm691CMqqAsKCuBwRB4HdXZ2wmaLPD746le/Kv+9evVqHD16dEBB3dNzap4cm80K\nu334O0hlhwUsz/Gqv+d2RR5PayHAbu9HKCySO+yuuO90hrMMXDAEu70ffPimxOUOyMv6AxxMGTrV\n3wuFrSGd9n5oYx691DeImYMMloFkxGlq7QXDid9ZnL0Yi7PFiYkcz2HnEQeAWnBBLuq3WC0Djy+o\n8vta3DH3WwAQ9dl35tyCR1/fAk3QLL/P8+HH+0EjtBoG3d1u8BwHrrsYV144HVWmGVHr+K+Z1yHI\nc3jmQKP8Hu+yoMxSgosqvwifU4AP/bAxRTijaDH2OWqh9Rnw93ePYk+dA6xW7Mq3cm4xZlREnqJI\nMNBjTn419ncehYaPbFsNWHAAMjQmdITf4wIh+XOBFyCEdBB4DRhOo7pPguFHrg6HC8tKlsJ2Vi7+\ndV7JEacAACAASURBVPBdHHHUgwGDq6ddjq528TjxeiPbVasB/P7IbxVqSuHyNMCgE4u9CQKD6VlT\nUWQpgKMpXDfc44fdLu53HasB484Fo22FJmiS1+MO30z19flQbhOtLlOzKuHs8Ys1znlBXra/X1yv\ns9+HorCPutRSDE8fBw/EZSaZK1DXfRL//Z8HUZBhgyUjA8ccJ9Hl64aG0WBlwVny+pYVLsLHrdvB\nBRkYNEzkmPaIMfX0+mDKMqGu+yTue1d8UjIlqxJXTP4K+rp9uHLaZXhqz7N47KPfwxPyxtWrNvHi\nxE9Xvw98iJfLML55bGt4PzM41nUivO8Y8EEM+jwxmHMLCW9iuJE6Jp5oc0KjYVDf4sSS6gK5yx0A\nNLT345nNtdjyWRMON/bi1q/MRmGO+iRiiY3v1mHXUTuqK7Jxx5XzoGO1uOb8Kry/owlbPmvE8jlF\nKM2PTtq4fUG0dXkwqzIHmSY9Lj2rEpcsnwQBoDJ5oxSGYXD2glL09Pvlm7NTIctiwOzKXBw40Y32\nbg+KcpMfX2ORERXUK1aswJNPPok1a9agtrYWBQUFsFjEbGx/fz++//3v43e/+x30ej0+//xzfOlL\nXxrJ8IYFqRZ1Qg91kkmJat0SI5YP6VG9+Pg6alLiAB5qQLQXxCK1P8/NNMq/7U7QYEar0cZVHJEw\nsNooT/dAlFqKoXVMj/LmshoNfPtXIC8nG15WzOLrWC3A6bCiZAViKwIWmcUJrL5ApEQbH9Tjf5Z9\nP+73bph5FQLcV2FkDfjnx8fRpWhmc6ypD+u/c6bqyf3mWdfix59tBcsoOhAKRmjts3D+orOxa3e4\nlrgx8s+K1WoQbKwG1zEJrDWB5UPaJ7xokzmzfBGmGqejxdWGIB/EpMxydLSIGdPoCYGaOOuOL8DB\nbNTBEN4nX6/+L2Sa9fjFzl0A4ieQss4KmLtKEUTk5kqqXx7keEzJqsTUrMk4u2yFHKMUrxi7ZCHi\nkWWw4uuzr0d+RnTL4DOLl6Cu9wQ6vXZ0esQbarPOhFm5M3Bm8RLkZ0QaBJxXcTaO9R5HZ3+uquXD\n6w9hTt5MHO4+hll5VZidV41ZedXQhe00M3OrsLhgPnZ27kUGm4FLJ1+A5SVL4ecC6A+4sOWjLnTB\nKTd24V3ZKDIWoyK7GEsKF6AqZxp6fX2o727Cs/+sA1sU3wGVIMYSJfkm6FgNjjT2YvcxBwx6La79\n4vSoZSYVWfHALUvw4ttH8cn+djy+cQ8e+taZCTPVn+xvw7u7mlFqM+POr82Tr29GPYvrzp+OJ1/b\nj79sOYIfXbcwqjvs8VZxcuSUkojdjmEYkJQe3Vx1zul5erB8dhEOnOjGttp2fHXVlNOyztHEiArq\nRYsWYfbs2VizZg0YhsEDDzyATZs2wWq14vzzz8fq1atxzTXXwGAwYNasWQNmp8cCJeE79EyT+oU5\nalKiJKjD5fO8SSclKpupaOPK5p2Kh1pq6pKXZZTFddKOjf5ocS+h02ng9g2uOxLH8dDrFEJVK1ax\n8PsjFSmkCiKBIA+DXn18vgCHDAMLrz8kxxeLhtHAyIo2i35vEGU2C+6+ej5e2VqHbbUdOHiiG3NU\n2t5msEYIXgu0+mhRi67pmJpdiY98hwFEfMriOBggaAQfNEKblahTokbeBkqU9bWl/cVqoycExjb/\n8Qc45GUa5U6aviCHTIhNegw6bdQFUjxuOPC8EFViMXLTxSODNeIHi78biYOLXlbp/waAxYXz48ZX\nYinCj5Z+D4BYE9ySrQfnUm/DbjPl4f4zf4hbP94KXb6iC6XiJvP2mVfFfU/JddVXYl7+LMzOr0YG\nG6m0U2iyYUvYPy+1HkfQgOsmfR1TSyMXeJspDwbBCr6/G9oSutQTYxutRoOKQgvqW0Qxe/U501Qn\n/hn1LL5xySzodVq8v6sFn+xvwxcWlMYt19Dejz9vOYIMA4s7rpgbd/5fON2GBdPysafOgT11Diyc\nHnkKXd/SBwCYWpLahHBifLGwKh96nQY1te34ysrJaYuDFwQcPNmN6WXZCZOdp8KIe6jXrl0b9bq6\nulr+++abb8bNN9880iENK9UV2fj+VfOiajMqGWyGWi6bF9Od0K+s8hESVLskAhEBpNaFUWrqkptp\nhMkgerU9SYSxT56EF31A6lkt/MHBzeTleCG6YUj4b1+Ak2t4S8I6EOIAFUEtCAJ8gRCK88zw+kPw\n+pNnyYMhDoEgjyyzDjlWA85fUo5ttR14b1eLqqAGxO3GapXCn4EvPOFP2lZSPXHx80iciUpBSQI2\npHKTo/xdILq2OKthop40cDyPQIiHUa+Vjw/pePH4g1FCHxAFtc8fAhjAqDgOtXKVj+h4BEEALwjR\nol4Tya6ngklnQp7JCrs7sSVCEAQEQtE3haxWFMDSE5pkGFkjlhQtVP0sGN6OUl1zAAiE4o9VaTza\nFLykBDHamVyUifoWJ0ptZpy3JLl/9ctnVeLjfW3496cncdac4qh/hy5vEE9t2o9giMdtX52T0BZy\n2cpK7Klz4PNDnVGCWspQK29giYmDUc9iUZUN22o7UN/qREFBem6s3qhpwKYPj+Mbl8zEirmn7guP\nha4WwwzDMJg3NT/uLl5CPUOduA51bNk8aR2SKOB5UfToEkzU1CpKosXS7fSB1WpgNelk20Iiy0ei\nWMR4NAhxvOzvToVQXDk2MU6vPyRPOpUFUAI7SSDIQxCAbIs+HF9y8eXyRtq9A6LXcHJxJvbWOeDo\n9ap+J75sXMR2IW0rU4zlQ142gaCOZKgTb6+IwIv+7ZDCB+9X7A9jrKD2heQbNQlD2CrExVTu0CU4\nRuQYVPZTbHZ9KEhCXh/zlCXDwMIzwE3SQEjHjk4hqIOqgjrcepx8ncQ4YPEMG/KzjLjlwuoBJxxm\nWww4Z2Epupx+fLwvUpEhGOLwu38cQJfTh6+snIz5SSpyTCq0Ij/LiL31DvnfFy8IqG91ojAnQ77W\nEROP5bPFCl01te1p+f3Gjn788+MTyLLokx7DpwIJ6jSjVjbPoBebWqhlh2PL5gFihloSCpIIGshD\nrZYN7XL6kZtpgIZhIrWwk9Qo9arEIsYjvg4OIksdK+qUmU+5WYxKV0gl0rYxGXXQ6zRyGbtEuL3h\njLLi5H7uolIIAN7f06L6ndiycVoNI4svjy8UZ6vQsdEWDTUiY028vSSxHSvQlSJcvsExaOXHWL5g\nCIIgwOvnosr5ARGrEMfzqrWl4wQ1F5+1TSW7PliCIUn0Rh9XGXrtaaiZy8tt46Wb2WAo/jjhVW4e\nCGKsMqMiB7/47lkpZ4YvOnMS9KwG/65pCD/J4/Cb1/bjUEMPFk7Px5dXVCb9PsMwWFRlg9fP4WC4\nTF57lwdefyjKP01MPGZV5iDTrMfnhzpVkxnDSTDE49l/HwTHC/ivi2ae9hs7EtRpRsMwkVbP4Z2r\nYZhwNm5wHmpBEFIQ1OrZ0GCIg9MdQG7YWyf5uFOxfMQJaqU1I0U4no/Jvkb+lsYiZU4TZahlC4pe\nC6OeTeihlnCFBbVFYdFYNrMAlgwdPtrbpiq0YuNktYycUXX74m0VUeJTpRW8tA4g3mIR9buy5UP5\n25ooES7dQBh10ZYPf5ADLwgqlg8xux4I8nHrVYtH+i21TonJsuuDRXraEnsMZxgG3qeprFs+nuQu\no8ksHySoiYlHllmPcxeXoaffj3d2NOPJ1/ah9kQ35k/Nw61fmZPSk5slM8Qa9juPiL0m6ltF//S0\nUvJPT2S0Gg3OmFkIlzeI3Uc6R/S3//nxCTTb3fjCghLMm6pu6xwKJKhHAQadWHFAaY43GdUFtccf\nnxU2sBrwggCOFxCUMpkJBHVEAEWLCGkSYl6m2IjGnKRbo4QkbuIzn/HNZpLBCwIEAaoeaiAi0GUB\nlEBQexUVUDL0kSokiZAFteIuVcdqsWpeMVzeIHYciW46xPNqcWpkMenxhaIqfACI8rIntHwoKmUk\nQnVSYlyGOjJ+eVJigJP3YazlQ8rQcrwQlflmE2SoJYEdW2lkoNgHi5S1ULN8BEK8ql1pMOvWx9yg\nqWVJpAw1tUAmJioXnlEBg06LV7bWo/ZkDxZMy8dtl89NmKyJZUppJrIteuw+ZkeI4+VJkZShJpbP\nEatybdpahw/3tmLbwXbsq+8a0rl9IOqa+/Cf7Q3IzzLi6tNUtSQWEtSjgAwDi0yzPuo9k4GNE7M8\nL6C+pU9sDa4QRxErBIdQguyehJx9jHlE3+2MlMwDIj7gVDzUhgSWj1Qz1FwSoQZEbg4kIZRowqPP\nH8mYGw2sLLAT4fLFC2oAWDpTzKwcD18A5DilDG2MSBZvZnh4/SE5sy/Hrlx2IMtHkixvSLYgRE/U\nU04G9CueGMgZ6iAn34RlqGSoY2NQxpzQQ60yplQnJaZCogy1yZB4bkGqBEOcbCWRrUnJMtTkoSYm\nKJkmvTyBceH0fNx2+ZyUxTQgPmldXFUAty+EI029ON7aB71Og7IC88BfJsY1kwqtKM03o/Z4F/7f\nfw7jD5sP4olX9uKDYeyiuPH9Y4AAfOOSmXFJwNNFWjslEiI3X1Qdd2djMrLwBbiwv1X8tKGjH25f\nCAurbFElxyRh4A9GsncJq3wkyD529UVK5gFi3WwNw8DjT275kJZTEpk8mNrdplzBIoH41MuTEhN7\nXqV4ANFDnKEXbTA8LyTMMqp5qAHIBefbu90xccYLfymb6/KGIABxGWq1ihixaFPwIatZPkT/tgBB\nEMAwTJQFR3qCkUqGWhmDGHMCy4ea7WQYLB+Rm8LoGzWpxb3XH4LVpI/7XioEQrx8A6VLYk3iKENN\nEPjqqsmYOSkHVeXZKXVPjGXxDBve3dWMT/a1ocXuDrcnpzzeRIdhGNx99XzYXQE4utxwugN4ZWs9\n6lr68MXFp7+LYr8ngOMtTkwvz1Zt3Ha6IEE9CphdmRv3XoacjeNgyRBPQNLkjtjllZ7l4EAeao16\n9lEqmSdZPpjwxMRklg9vIBTnnwYiTWxSnXCgVj1C6TeWPa8DVPmQLA8ZelauPOILxGeNJdQsH4Bo\nmci26NHeHd1lL2K7iC8x53QHACDOpxw9iXBw+2Tg345kh8XyfWHLh4GNTEoMcHJGN05Q66Kz3ZG/\nE0xKVKvyIcV+Gi0fksBVxgdE/5s49XXz8s1msiof/AT1UK9fvx579+4FwzBYt24d5s2bF7fML3/5\nS+zZswcbNmzA9u3bcdddd2H6dLFRSFVVFf73f/93pMMmhgmtRoNZKtenVKkqz4bVpMP2gx0QINpA\nCAIQn4bPmGqD3d4PQRDw75oGnGwfnu7Vhxp6IACYPfnUj+VUIEE9SlGWzpMEX+0JUVDPrIy+w1JW\nv5BsCYnL5qk/onf0iWXipAw1IGZbB7J8ZKgIakmo+FO1fKiWY4uflDiQ5cOrzNDK2UwuoaB2y2Xz\n4v8ZFOWacLixF/4gJ4vTRFliAHB6REFtPgXLh5TVdnkSPw2IVNhQt2awWk1Mhlpcpz+QzPKhyFCr\nWD5iffahZKJ+OCYlxmTETovlI8jLXnx90rJ5E6/Kx2effYaGhgZs3LgR9fX1WLduHTZu3Bi1TF1d\nHT7//HPoFE2Yli1bht/85jcjHS4xBtBoGCycbsOHe8VH+VPJP02owDAMJhVacLixF15/6LRbMg6E\ntdOcYRbU9OxllCIdUFKG2B/kUNfSh4pCCzJjHnfrFZP1QiFRCAy2yods+ciMdNAaKEPtC4TkltBR\n8UjWjBQtH5JwS1SzOTZDndjyoZyUyEa9p0aiDDUAFOWJPr/Onkg96kS1oAGgP2GGemDLR274Jkbq\nVKlGKImYl+KSPe26iOXDHwwlsXyob2/p72AKlo/BNnZJBUng6hJmqE9NUHO8WBtdOj6l/6tZk2TL\nxwTyUNfU1OC8884DAEydOhV9fX1wuVxRyzz88MO4++670xEeMUZZMiPS2IU6JBKJqCwSj43GjtOb\npRYEAbUnumHJ0GFSofW0rjsWEtSjlNhuiceaehHiBFV7iIGNTEoc2PKRwEPt9CHLrI/KbJuMOoQ4\nXtViwYfLrallqGWBP5QMtUqVD0kASV0hP9rXioc27JDjkyYlZhgUGeoklT5cviAYJr5KCaD0UUds\nH9I2Y2O6FQIRy0dchlqxHxL5caVShVKlFTXUJwRGe52VNcoNSg91AsuHsqqMcr0MwyDDoI0rmah2\nQ8EwTFQt7tNBMEGGWhbUKXRLVEMSztK/DWnfBFWsNhOxDrXD4UBOTuTpV25uLuz2SKWbTZs2Ydmy\nZSgtjW5HXVdXh1tvvRXXXnstPvnkkxGLlxgbVE/KgSVDh8JcE7Is8S3PCQIAJhWJYvd02z5auzzo\n6fdjVmXOsM+JIcvHKCVSB1oUD7Vh/7San02elBiK1BNONIFErVMiLwjodvrlA1pC2S1RH9PvXpkN\njosnRvgORLLML6DmoRZj33nEjvoWJ9q6PJhUZI0qm2dMIUPt9gZhNupUs5CyoO6KTExUj1OyfIji\nMy5DHSW+1feJjtUi06STfexqRMS82oRA8TNl50pJLPsVHupklg825kSTbTGg1xWIeo9TqTQivmZO\nr+UjKHmoYxq7DNFDHYypHiJbPlSOU2o9LmZ2JHp7e7Fp0yb86U9/QkdHh/x+ZWUl7rjjDlx00UVo\namrCTTfdhLfeegt6feJJozk5Jrn76WCx2YY3wzQYKBZ11GJ55I6VYLUa2GyWtMeSLigWdaRYFs1i\ngM216Oj1ndb4Pj0k1rpePq9kwPUO9XdJUI9SIhlqUajVnugBq9Vgelm8B01p+WBjJlzFouah7nMF\nwPGCPCExPoYQcqzRmYVETV2U8aQ6KTFiZ4jP/IpjUW897gjbVLr7fZhUZI2KScqc+5KIL5c3mLBT\nUlFuBoDoDHWy8n6RDHWMoE6hUyIA5GQa0WJ3R4kYJcl85qEYy4eyU6I/mKzKR2J/d7bFgLYuT1SZ\nOTXLh/Td02r5SPCUJSP81CFZ985kyJMdYy1EKhnqidh6vKCgAA6HQ37d2dkJm018XL9t2zZ0d3fj\n+uuvRyAQQGNjI9avX49169bh4osvBgBUVFQgPz8fHR0dKC8vT/g7PT2ehJ8lw2azwm4fnklLg4Vi\nUSdRLBlaBoAwonGOhe2SDkZrLFpBgFGvxZGG7tMa3/b9bQCA8jxT0vWmul2Sie6Jm34Z5UiZzq27\nW7HzSCea7S5UlWfFZe2AaC+oVAmkrEA9ExCxfEQEkNqERDGGxN0S5QmASTzUqZbNUxeqCkGtjc4o\nBoIcBEGQfd9SDW21SXmJ/LaCIMDtDSUU1PlZGWC1TLTlg1fxessZaslDHb2+VBq7AGJ1lRDHoz/B\nxERVy4fUVEUlQ63RMNDrNNGWj5jYoiclRp8KpBuoHkWWOpSg8oVWozm9gjqYwPIxwD4dcL0x5fj0\nSco7TkTLx4oVK7BlyxYAQG1tLQoKCmCxiOeRCy+8EG+88QZefvllPPXUU5g9ezbWrVuHzZs347nn\nngMA2O12dHV1obCwMG1jIAhibKJhGEwqtMot6k8HwRCPI409KMk3yz02hhPKUI9SZlRkY/bkXNSe\n6MbTfz8AQL28HhDJCLu8QWyr7UCWRY9FMwrQHVNHGVCv4CAJ0/wYQW1O0txF6deNRXeqHuoElg9p\nfDqFUHd5g7KlpKdfEtSRsnFSNjNRt0SvX2zHHZtRltBoGBTkmNDe7ZHrPKsJf8nG0e9Rz1DHNoFJ\nRG54MmiX04epKp9HanXH18COTEqM3idGnRb+IAdv+IbIZIhtwJM4NklQ9/b7UZAtZuvVxi+9Pp0d\nrqSMcaKyeafafjy2A2OyuuYTsfX4okWLMHv2bKxZswYMw+CBBx7Apk2bYLVacf7556t+59xzz8Xa\ntWvx7rvvIhgM4ic/+UlSuwdBEEQiJhVZcaSpF02dLlSVZ6suwwsCGCCqF0cijjX3IhDih726hwQJ\n6lGK2ajDf1+zAHUtffj3pydxvNWJxYrZ0kqkjPC22nZ4/CFcsmhSQu+nWtOO2BrUEpLn1qsqqBNb\nPgyDzVDz8ZaPqLJ5MTaWQIiT7R4A0NMv/u3zc2AYUTAZB5jAlqhLopKiXBNaHWLR+SyLIWkDmkgd\n6iQZ6iR+3FyrVOlDfWKiJGaVPuxIhjpi+ZDGD4gdLMUMtWgFii2lqGzsEuu5zw5PHupRTJRU20/i\nuIbHQx0bb0bMRN1BrzfGQ81qGTCg1uNK1q5dG/W6uro6bpmysjJs2LABAGCxWPDMM8+MSGwEQYxv\nKhUTEyVB3dzpwmMv7YbHHwo3MhO1yoVnVGD1/OKEJYKBSKnh4a4/LUGCepQzrTQL379qftJlpExe\nY6dY4mrlvOKEy0bKnMVnqGMtH1LFCreK5UPKEqpNSkzWgU4NWSwmKDEnZbwlARQIcnLMgNLyEYJR\nz4JhmEinwAQe6kRdEpUoK31kWQyR5ioq1pQ+d3h9p1A2D4hs+0Sl85JNiJSsKD4/B6NeK9+5G3Qs\n3F4fPP5Q3GRJICZDreKhBoBel0JQq+wnQBT5qe7rVIidPChhGuqkRFmoi+tlGAY6ViMLbSXUepwg\nCGJkkQojNLQ75ff++fEJOD1BTCqyQs9qwDAMTrY58eLbR/HvmpO4aFkFzltSrpr8qD3RDVarSZjt\nPt2QoB4HKL2w1RXZKMwxJVw20npc6aFWz1BLIkytFnUqkxJTz1AP0ClRK/4GwzDQ6TQIBGMz1BEP\ntWT1GKgOdbIa1BJKQT2jIkdV1Cqbq+h1mrhMb6La2rFIlo/ufnVBrVaHWrZ8hPelPxiKusEx6kXL\nh9YXjCvnB8S0Hk9g+YjOUKtXvtBqGXCB6Ax1MMThxbeP4ZyFpXHVYwYi1pohx6vTQMMwp142T1qv\n4t+LjtUkbewy0TLUBEEQ6aIw1wSjXiuXzmuxu7DzqB2TizNx302L5WSR0x3Als8b8d6uFrz0Xh30\nOi3OXhhdzrPPHUBjpwuzKnOiSsQOJzQpcRygFB6r5pckXVbVQ+30wWxk4+oxS9lWtUfskqBWq+Gs\nT+JNVSOZlQKIzqTqtGJGUZpIqddp0N3vhyAI8AU4WVBGaharxzBYQR0Vp0ZdJKuJVrU25WpIlo+u\nRJaPJG2/lZMSlTc4Br0WHC/A5Q2q76dUPNQuNctHvIc61vJxpLEXH+5txfu7W1THk4xYa4aEVB97\n6JMSo0syUutxgiCI9KNhGFSEJyb6AiG8vq0BAHDpWZOiPNOZZj2uOnsafnzjYgBAXUtf3LqONfUC\nAGZOyon7bLggQT0OkO6+TAYWi6vUfdYSckY1LBikahmxdg9pfUACy0eSSYmnI0Ot1thFXLc2KkM9\npThTrI7hDcLrD0Um5MmWjyFkqPOkWtSioE7W/huIr0ENRDd2SZahzrLoodUwiS0fnFhjXHlSYVXK\n5in3hzF8XAiCemzKu/bYzHqmWQeGiclQJ5iUaDLq4PWHom6gpJuQzlMokSatR630Y4aBHbKgjjqe\nWK2qXSVRzW2CIAhi+KgsskKA2Gdi+8EOlNnMmD8tX3XZkjwz9KwGzXZX3GeSBVbqwDgS0NViHJBt\nNSA304ALlparltVTInuow1nNfm8QgRAfZ/cA4pvLKJF8rKqCWpqUmGId6mTZVyBalOpYjeyhNhlY\nlIYbBdh7vOB4QY5H6hSYKEMte6gTVPkARLFtydBFMtRqZfOUGWqVLHCqHmoNwyDHakgoqEO8oFqu\nDhCFLsfzCIb4KMuHQbFvYmtQA7Fl8+LXnWXWR2Wo+6Ra2zE3IcV5JggAOrojbdqlv5VlB1MlInzj\nj62hCOqAilAf0PJBHmqCIIgRQ2oP/rd3jkEQgEvPqkx4HtZoGBTnm9Hq8MR1620OC+ryBCWEhwMS\n1OMAg06LR797Fr68onLAZdkYD3WiCYmAorFL0rJ5KpMSFY1mUoFTsXwohahSWOlYDfxBHo5wVl3y\nHrc6xBKBkndawzBilYsE4svtFd9PNikREG0f9l4fQhyvXi9bEWdshQ8g9TrUAJCbaUSfK6Au8Dgh\nrtNipEkPL1twlFnnKEGtNimRTW5HybYY0NMfkJvNSNu4NN8ctVxx2BrTphDP7eHMdK8rkLRbpRqJ\nLB8AkKHXwhcueThYAipCnSwfBEEQowdpzo3HH0JhrglLZhQkXb4s34wQx6Ozxxv1flOnC1lmPTLN\nI1fGkwT1OIFhmJTqMsqtx8N3c5Ea1Blxy2o0omdVvQ512EOtkqHWMAxYrXr1BDUGsnzooh7Ra+AO\n16DOzzLKXt/WcItwZcY8I1w2To1UyuYBoqDmBQH2Xq/65EmFEFXLdqfqoQaAvEwDBKhX+uB4Pk7c\nKZv0SNVMjIpa01HbQiVDrYsS1PHHTo5VLBUo7f8Whxt6nSbu5qsoTxTYbYo27R0KcR17ohuIRFU+\nAHEcAsSW6oNFbb36cJWP2A6VifziBEEQxPBRlGuSE0MXn1kx4MRw6Sl1iz1y/fH4guhy+hI2uBsu\nSFBPMGIrQySq8CFhMujk9udKfEk6JQKAQRcppdbR7ZHrNKshl6NL1Ngl6hF9RCTmZRnlyXxtYZ+z\nMmOeYWAT16FOwUMNiHYGQPzHKmXSE1k+1DLUqVo+AMidnOwqvmOOE+J8ztI24ngevmCkS6KEUZfc\n8sEwjOx3V4stW1Hpg+cFtHV5UJxnjnv8VhzjNQ+GossaDtb2EQzxYBj1mOTa6Kdg+1DzUEviOsTF\nCmqq8kEQBDHSaDQMFk7PR5nNguWziwZcvqxATOgofdTNYXE9knYPgMrmTTgkMSSJQ6mpS2yXRAmz\nkUVnb3yGMdmkRCD8KD3I48DxLvzmtX2YVGjFj29aorosN0D1jFgPtUR+VgZyrdGWj9gMrbK8nhKX\nNwg9qxnQc15ZLE5oON7mlLPhiTLpA2aoUxXUvV4Uhq0sEhzPq1bXAMIZapX9YVCIazXLBxCelBfk\n4+wkAJCjaO6iYzUIcXyc3QMQb8Z0rEa+qens8UKAmOHu6fejI4UM9Yk2J7LMeuRmGhEIcdCzsxQt\nVgAAIABJREFUWtUnLpKlx+MPYbCl+iMeaqXlI1KRRnls8VSHmiAIIi18+7LZ4AUhpTksZeEMdbMi\nQ90k+adt4zxDvX79elxzzTVYs2YN9u3bp7rML3/5S9x4440jHNnEgGEYsFpGrgyRzEMNiFlKX4CL\nE9VePwdWy8RlTSX0Oi16XX48uWk/QpyA+lZnlCXA0evFH/91ED39fjk7mMibrGfVs9X5WUZkWw1g\nFOOIqnKhZxEM8QhxPLr6fPjrO0flzKbbGxzQPw2IM44ZAMdbnadW5SNBKUA1pJsDh8oNTIgT4gS1\nZONo7/ao1gU3DjApERCfJCSKTVk6T3qcpiaoNRoGhTkmtHW7wQsC2sMTEudPzQMQbf9Qw+kOYP2G\nnfjTfw4DEDPJanYPQNl+/BQsH0EVy4fk94+xJ03E1uMEQRCjhVQnhGeZ9TAb2agMdVOnWMd6pDPU\nIyqoP/vsMzQ0NGDjxo146KGH8NBDD8UtU1dXh88//3wkw5pwaLUa9HsCCIZbeBv02oTVLqQJATUH\n2qPel7oSJkLypvK8gFXhzo3bajvkzzd9dBw1te14Z2eTehdAjXpWWhcjqFmtBplmPaQH9rGNTcRY\nOby+rQHv7GjGp+FxuLzqzU5iyTCwKLGZcbLdKZdzS1jlQ2V9Gg0jnxgGKsGWJ1s+4gU1x8dbPuZM\nzkWmWY/3d7cobigUVT50ySclApFKH2qxyd0S+/1odYgnqxIVQQ2Ito9AkEdvvx8dYcvK7Mm50GoY\n+XUi9h/vAscLONbcixDHDyCoxXhPpf14pLFLdF1zIL79OFk+CIIgRj8Mw6DMZoG9xwt/2PrY1OmG\nVsPIpW9HihEV1DU1NTjvvPMAAFOnTkVfXx9cruj6gQ8//DDuvvvukQxrwjGjPBv2Xh8e2rAT9j4v\n8jONCSc0Lp5hg16nwSf726IqK8TWPI7FatKDYYDvXDYb151XBYNOi5radgiCgG6nD58f6gQAfHaw\nI2nDFK2GSSiuJZuKlEkF1CfieXxB7DpqBwAcON6FECdWxbBkpOZ4mlKciUCQR2OHKyo2ILbKh/r6\nWDYylmQoLR9Kevr9cHmDyLJEz1bW67S46IwK+AMc/hMugB/b2EVCbVIiENmeaplY2UPt8qMlQYUP\nCclH3dblkT3TJflm5GcZo8rpqbH/eBcAsW55U6cLgRAf1yVRItJ+PF5QH23qVa3YIaHa2EWnXuKR\nV5mAShAEQYw+ymwWCBCtnzwvoMXuQkm+OeET9OFiRH/N4XAgJyfStSY3Nxd2u11+vWnTJixbtgyl\npaVqXydOE7dfPger5xejscMFf4BLaPcARCG2ZEYBHH0+ufMQgKiuhGr810XVuP/mpVhSXQCDXotF\nVTY4+nyob3HinZ3N4HgBWWY9upx+HGnsARCToQ7/zcYIK6nkWYaBlScBKgW1UjhKftv9x7vlSZGH\nGnvkvweakCgxpUT0UR9rFsefqHJHooy3lAUdyD5gMrKi7ztGUO88It58LJwe37Tn7AWlsGToZJ+y\nWmMXILHlQ8pQsyrCUemhbnW4YdBpkZvgWJGb4HR70NHtAcMAtuwMFOaa4PIG5UmgsXA8j9oT3fLr\nuua+cIZa/WbNKHfAjBbU++odePjFXXi95qTq94BIw5iosnlS1ZtEGWryUBMEQYxqShUTEzt7vQiE\n+BG3ewBpnpSoLFXV29uLTZs24U9/+hM6OjqSfCuanBwT2AQX34Gw2ayn9L2xQrLx/fCmZVj4WSOe\n+fs+zJtuS7rsxSun4NMD7dh5rAsrF1dAEAT4AyFYzfqE34t9/8IVk1FT245PD3Xg89p2ZFsNuPPq\nBfi/57bjYIMoqPNyzfL3BDbS8VC5rqxwFrcozyS/X1poxe5jDvF9m1V+PzdHLAVYc1A8niqLM3Gy\nzYljbaK/Kl/xe8lYPLsYL7x5BE6PKArz8iLfy1O0Ci8ryVJdn06nBXwh5OdZBvy9glwT7L3eqOX2\nnegGwwDnL69Enkp5wyvPnY4XXj8IAChUjL9XUe6wvDRb9bsWk5j1zlXZFoIgNsrpcwfQ3u3F5JJM\nFBaod52aNVUUq72eIOy9PhTlmlFclIXJpdnYV9+FgBB9TEh/HzrRDbcvhAVVNuw5akejw41giIcp\ng1XdVkUFYqZcy0YfFzvfPAIA2HHEjm9ePk/9iUv4SUdxUaZ8M5YdvkEwWQxR69OHhbstf+B9psZ4\nP7cQBEGMFsoUpfOkRF/ZCE9IBEZYUBcUFMDhcMivOzs7YbOJWbdt27ahu7sb119/PQKBABobG7F+\n/XqsW7cu6Tp7TqG1MSBe8Oz2/lP67lgglfHNn5yD39y5EqxWk3TZoiwD8jIN+GhvC65cNRkAwAuA\nVoOUt2FJtgFZZj227mwGAFy+tBwVeSZkmvVyxtjV75PX1xfu0KfVMFG/EQxnJrPNevn9DEUW2+cN\nyO/z4Yzk8ZY+mI0sLjurEr95bR/e2S7aI7QQUorfpGVg0Gllf1a/0yt/z6WoGe33+KF44CIjJab7\n+7ywG5Lf/GWadGhs70djcw8yDCz63AHU1ndhalkW+EBINd5lVfl49V0Wbl8IfsX4ve6I2Pe6/LCr\nlBBkwje1ym2vJMusR2N7PwQABdnGhNvLEB7jgXoHel1+lBdYYLf3I9MojvfwcQdyTaKIVR6bH+5q\nBACsmlOE4y19OFDvQIjjwQjqx1YwXD/c3u2WP/cFQth+oA2A+Mhvz8F21fqjbo94nPX1euCWvNPh\nbdLpcMGmsNS4w9uur88L+yAnJg7m3ELCmyAIYmhIVsRmu0ueI1NeOPKCekQtHytWrMCWLVsAALW1\ntSgoKIDFIg76wgsvxBtvvIGXX34ZTz31FGbPnj2gmCaGji5BeTIlGobB8jnF8Ac4fH64U37cnpHE\n8hGLVqPBGbMKAYgTFs9ZVAaNhsGymQWKZeLL0cU++pe8tcq62TmZ6h5qpSVl4XQbZlXmQMdqUN/q\nBJC65UOjYVBZFBE+yhJzA3VKFJdJzfIBRMYlNUPZddQOAUjaLSrDwOLy1VNgydChKDcyCUOalKhR\n1JuORZ6UmCC2HKtBnvBZmp/4BGXQa5GXaUBDuygkC3Mzwv+XrCDqPup99V1gtQxmVuZgemkW+lyi\n6B2oyofXF6nyseeYA4EQjzKbeFLdEbbIxBIIcXLTIQnpd2hSIkEQxNgkw8AiL9OIFrsbzZ3hGtRp\nyFCPqKBetGgRZs+ejTVr1uBnP/sZHnjgAWzatAlvv/32SIZBnAIr5ooF1p9/4xB+8NQnABLXoE7E\nWXOKwDDA6gUlspg9c1akcHu0oBYPTV3MpAJJYCvrZkvNXYAYD7UiG7yk2ga9TosZ5dnye6lU+ZCQ\nfNRAbDUS8W89q0koAqUxpCLOqsrE+F585yhCHC/7pxdXxfunlZy7qAy/vnNlVJtVaf+YjGzCmybp\nBkWtDjUQmZgIJK7wISF1TAQgC3vp/50qT5J6XX40drhQVZ4No57FtLIs+bOEgjo8JqWHenvY0vNf\nF8+EjtVgxxGVxwQIl+PTqXvygyEe/Z4APjskTpKVJiWqecsJgiCI0UWZzYw+dwDHmntHvOW4xIh7\nqNeuXRv1urq6Om6ZsrIybNiwYaRCIlKgMMeEK1ZPwdGmXgSCHEK8gKUzE2dN1agotOLn31ku11sG\ngMnFVhTkZKCzxxs1wU8SrbGZVSnzOVkhcHMTVfkIZ6gzDCxmThLbgMyZkocD4UlwqWaogVhBHYlJ\nynYmqvChXCaVihFnzi7E4eY+fLSnBS+8eRiHG3oxuTgz6cRRiVjRLGWfE01IBICs8MTDRNtCmpgI\nJK7wIVGca5InGEqZ6WyrATpWo9otUaruMXeKWK9aKagTNdyJ7ZTo8gZx4EQ3KgosmFyciTmTc7H7\nmAMtDjdK883o7PHgUEMPcjON8PhCKjdo4uuuPh8e+etutDrcOHNWIWWoCYIgxhBlBRbsre+C2xfC\nnMmDbft1eqBOiUTKXHpW5ZDXUZAdPTGOYRh8+axKvLerOSrrLLXsji2ftmBaPv78wJcQUrRDV2ZR\nlaXiJJG7YFq+LJyU/9AGJ6gjYo9VKZuXLNstjWWgOtSAuD2+d/UC1DX14JP9Ys3sJdXJs9OJf1cD\no14LiylxbJcun4Sl1QVJG/sA4o1Kbkz3xliKFTU/i3LEvzUMg8KcDHT0eCEIgiz6fYGQXJd8XrgB\nzKRCq9hhM8THCV8J6SZJEtQ7j3SC4wUsC9uJllYXYPcxB3Ye6YTXl4tfvbI3qsReXswYpOPitQ/q\nwfECrCYdth3skG9+SFATBEGMfpQJH7U5NCMBCWoi7ayYW4wVc4uj3tNqNPjqqslxPiiGYZCTaYTd\nHhHUUnMXf5CLKnM2rSwLl62oxMp5kXUX55mQl2lAl9MPc4p1qAHRSyy10lbzeqeUoU5xcluGgcXt\nl8/Fg3/eAX+Aw+Ik/umBuPUrs2E1JX70pddpk5YXkjLUJfnmAb32kuVDx2qifO2FOSY0293ocwdg\n1Gvxn09P4C9vHobTHUBlkVW2hbBaDSYXZ+JoU2+cNUNCo2Fg0Gvh9XPw+IJywyHJiz9/Wj5YLYOt\nu1vwxrYGhEICLg9PpLX3+lA9KTtqfdING8cLOGdhKS5fPQW/+OsuuY0t1aEmCIIY/SireqSjZB5A\ngpoYxVy2YnLKyy6rLoDLF13rWBTlU6LeYxgGq+aV4OP9bVETG1NhVmUOdh6xy7WQgYhYTpqhln3K\nqYuzknwz7r5qPjp7vHFZ/cEwb2r+KX8XiEz4HMg/DUQy1IU5GVE3NpL943+f3Q53uJSfQafFV1ZO\nxpeWlUcJ9ellWaKgTlKQP0OvRWNHP+544iMAwLTSLOSHSwJmGFjMmZyHPXUOsFoN7rhiLhZMT7wN\npO+tnFeM6y+ogoZh8N/XLMDPX9yFXpc/qmY1QRAEMTopyjNBq2HA8UJaJiQCJKiJccJ151elvOyX\nV1TispWpi3WJ68+vwuWrpkS19M4067GkugBLZiS2ZeRnGWE2sgl9wYmoKs9GVXn2wAsOI5OLM/G1\ns6cOOCkSEEvsrZhbhMnF0bWqZ1fm4J0dTTDqWVQWWVE9OQ8rZxfK/m0lVeXZeL2mAeYkdpw5k/Ow\nt96BigILKgqtUU8gAODCMyrQ6/LjqnOmYeaknARrEZlUZMUT31sJq0knC/ssiwH337wUTk8g4eRI\ngiAIYvTAajUozTejvdsz4i3HJRhB2V1lDHKqtaSpDvXYZayNjeN5+ANcwrJ6sYy18aWC0j+dbHyC\nIODzw52YVZk7KI/7aILqUCdnPJyzKRZ1KBZ1KBZ1TncsTZ0ueHxBzKhInkgZSizJztmUoSaIYUar\n0cBknNiZzoH818rlls0sHOZoCIIgiPFGurzTEhP7Kk8QBEEQBEEQQ4QENUEQBEEQBEEMARLUBEEQ\nBEEQBDEESFATBEEQBEEQxBAgQU0QBEEQBEEQQ4AENUEQBEEQBEEMARLUBEEQBEEQBDEExnxjF4Ig\nCIIgCIJIJ5ShJgiCIAiCIIghQIKaIAiCIAiCIIYACWqCIAiCIAiCGAIkqAmCIAiCIAhiCJCgJgiC\nIAiCIIghQIKaIAiCIAiCIIYAm+4ARpr169dj7969YBgG69atw7x589Id0pD5xS9+gZ07dyIUCuE7\n3/kO5s6dix/96EfgOA42mw2PPvoo9Hp9usMcEj6fD5deeiluu+02LF++fFyNb/PmzXj22WfBsizu\nvPNOzJgxY9yMz+1245577kFfXx+CwSBuv/122Gw2/OQnPwEAzJgxAz/96U/TG+QpcPToUdx22224\n5ZZbcMMNN6CtrU11n23evBkvvPACNBoNrr76alx11VXpDn3MMRrO2anu75FgtJzvvV4v7r33XnR1\ndcHv9+O2225DdXV1Ws9do+E6sX37dtx1112YPn06AKCqqgrf/OY307ZdRsv15ZVXXsHmzZvl1wcO\nHMDf/va3tFwLhu26JEwgtm/fLnz7298WBEEQ6urqhKuvvjrNEQ2dmpoa4Zvf/KYgCILQ3d0tfOEL\nXxDuvfde4Y033hAEQRB++ctfCi+++GI6QzwtPP7448IVV1whvPbaa+NqfN3d3cIFF1wg9Pf3Cx0d\nHcJ99903rsa3YcMG4bHHHhMEQRDa29uFL33pS8INN9wg7N27VxAEQfjBD34gbN26NZ0hDhq32y3c\ncMMNwn333Sds2LBBEARBdZ+53W7hggsuEJxOp+D1eoVLLrlE6OnpSWfoY47RcM5OdX+PBKPpfP/6\n668Lf/jDHwRBEITm5mbhggsuSPu5azRcJ7Zt2yZ873vfi3ovXbGM1uvL9u3bhZ/85CdpuxYM13Vp\nQlk+ampqcN555wEApk6dir6+PrhcrjRHNTSWLl2KX//61wCAzMxMeL1ebN++HV/84hcBAOeccw5q\namrSGeKQqa+vR11dHc4++2wAGFfjq6mpwfLly2GxWFBQUIAHH3xwXI0vJycHvb29AACn04ns7Gy0\ntLTIWcaxOD69Xo8//vGPKCgokN9T22d79+7F3LlzYbVaYTQasWjRIuzatStdYY9JRsM5O9X9PRKM\npvP9xRdfjG9961sAgLa2NhQWFqb13DWarxPpimW0Xl+efvppfOtb30rbtWC4rksTSlA7HA7k5OTI\nr3Nzc2G329MY0dDRarUwmUwAgFdffRWrV6+G1+uVH+Hk5eWN+TE+8sgjuPfee+XX42l8zc3N8Pl8\nuPXWW3HdddehpqZmXI3vkksuQWtrK84//3zccMMN+NGPfoTMzEz587E4PpZlYTQao95T22cOhwO5\nubnyMuPhfDPSjIZzdqr7eyQYjef7NWvWYO3atVi3bl1aYxlN14m6ujrceuutuPbaa/HJJ5+kLZbR\neH3Zt28fiouLodVq03YtGK7r0oTzUCsRxlHX9XfeeQevvvoqnn/+eVxwwQXy+2N9jP/4xz+wYMEC\nlJeXq34+1scHAL29vXjqqafQ2tqKm266KWpMY318//znP1FSUoLnnnsOhw8fxu233w6r1Sp/PtbH\np0aiMY3HsY40o3EbpiOm0XS+f+mll3Do0CH88Ic/TNu5azRdJyorK3HHHXfgoosuQlNTE2666SZw\nHJeWWIDRd3159dVXcfnll8e9P5KxDNd1aUIJ6oKCAjgcDvl1Z2cnbDZbGiM6PXz00Ud45pln8Oyz\nz8JqtcJkMsHn88FoNKKjoyPqUeVYY+vWrWhqasLWrVvR3t4OvV4/rsaXl5eHhQsXgmVZVFRUwGw2\nQ6vVjpvx7dq1CytXrgQAVFdXw+/3IxQKyZ+P9fFJqB2TauebBQsWpDHKscdoPWen8xw0Ws73Bw4c\nQF5eHoqLizFz5kxwHAez2ZyWWEbTdaKwsBAXX3wxAKCiogL5+fnYv39/WmIZjdeX7du347777gPD\nMLLtAhjZa8FwXZcmlOVjxYoV2LJlCwCgtrYWBQUFsFgsaY5qaPT39+MXv/gFfv/73yM7OxsAcNZZ\nZ8njfOutt7Bq1ap0hjgknnjiCbz22mt4+eWXcdVVV+G2224bV+NbuXIltm3bBp7n0dPTA4/HM67G\nN2nSJOzduxcA0NLSArPZjKlTp2LHjh0Axv74JNT22fz587F//344nU643W7s2rULS5YsSXOkY4vR\nes5O17/R0XS+37FjB55//nkAojUnneeu0XSd2Lx5M5577jkAgN1uR1dXF6644oq0xDLari8dHR0w\nm83Q6/XQ6XSYMmVKWq4Fw3VdYoTR+AxtGHnsscewY8cOMAyDBx54ANXV1ekOaUhs3LgRTz75JCZP\nniy/9/DDD+O+++6D3+9HSUkJfv7zn0On06UxytPDk08+idLSUqxcuRL33HPPuBnfSy+9hFdffRUA\n8N3vfhdz584dN+Nzu91Yt24durq6EAqFcNddd8Fms+H+++8Hz/OYP38+/ud//ifdYQ6KAwcO4JFH\nHkFLSwtYlkVhYSEee+wx3HvvvXH77M0338Rzzz0HhmFwww034LLLLkt3+GOOdJ+zB7O/h5vRdL73\n+Xz48Y9/jLa2Nvh8Ptxxxx2YM2dO2s9d6b5OuFwurF27Fk6nE8FgEHfccQdmzpyZtu0ymq4vBw4c\nwBNPPIFnn30WgOg1T8e1YLiuSxNOUBMEQRAEQRDE6WRCWT4IgiAIgiAI4nRDgpogCIIgCIIghgAJ\naoIgCIIgCIIYAiSoCYIgCIIgCGIIkKAmCIIgCIIgiCFAgpoghsimTZuwdu3adIdBEARBpACds4nh\ngAQ1QRAEQRAEQQyBCdV6nJjYbNiwAf/5z3/AcRymTJmCb37zm/jOd76D1atX4/DhwwCAX/3qVygs\nLMTWrVvx9NNPw2g0IiMjAw8++CAKCwuxd+9erF+/HjqdDllZWXjkkUcARIr519fXo6SkBE899RQY\nhknncAmCIMY0dM4mxhKUoSYmBPv27cPbb7+NF198ERs3boTVasWnn36KpqYmXHHFFfjrX/+KZcuW\n4fnnn4fX68V9992HJ598Ehs2bMDq1avxxBNPAAB++MMf4sEHH8Rf/vIXLF26FB988AEAsePTgw8+\niE2bNuHYsWOora1N53AJgiDGNHTOJsYalKEmJgTbt29HY2MjbrrpJgCAx+NBR0cHsrOzMWfOHADA\nokWL8MILL+DkyZPIy8tDUVERAGDZsmV46aWX0N3dDafTiaqqKgDALbfcAkD0482dOxcZGRkAgMLC\nQvT394/wCAmCIMYPdM4mxhokqIkJgV6vx7nnnov7779ffq+5uRlXXHGF/FoQBDAME/fYT/m+IAiq\n69dqtXHfIQiCIE4NOmcTYw2yfBATgkWLFuHDDz+E2+0GALz44ouw2+3o6+vDwYMHAQC7du3CjBkz\nUFlZia6uLrS2tgIAampqMH/+fOTk5CA7Oxv79u0DADz//PN48cUX0zMggiCIcQyds4mxBmWoiQnB\n3Llzcf311+PGG2+EwWBAQUEBzjjjDBQWFmLTpk14+OGHIQgCHn/8cRiNRjz00EO4++67odfrYTKZ\n8NBDDwEAHn30Uaxfvx4sy8JqteLRRx/FW2+9lebREQRBjC/onE2MNRiBnnMQE5Tm5mZcd911+PDD\nD9MdCkEQBDEAdM4mRjNk+SAIgiAIgiCIIUAZaoIgCIIgCIIYApShJgiCIAiCIIghQIKaIAiCIAiC\nIIYACWqCIAiCIAiCGAIkqAmCIAiCIAhiCJCgJgiCIAiCIIghQIKaIAiCIAiCIIYACWqCIAiCIAiC\nGAJjvvW43d5/St/LyTGhp8dzmqMZPYzn8Y3nsQE0vrHOYMZns1mHOZrRx3g4Z1Ms6lAs6lAs6ozF\nWJKdsydshppltekOYVgZz+Mbz2MDaHxjnfE+vnQxmrYrxaIOxaIOxaLOeItlwgpqgiAIgiAIgjgd\nkKAmCIIgCIIgiCFAgpogCIIgCIIghgAJaoIgCIIgCIIYAiSoCYIgCIIgCGIIkKAmCIIgCIIgiCFA\ngpogiFGDP8jh+dcPobnTle5QCIIgiDHEh3tb8Y+Pjqft90lQDxNbt76b0nK//vUv0draMszREMOJ\nxxcELwgJP7f3enHwZPcIRpQ+AkEOIY6Pes/R58ULbx6Gvdc74PePNPbg4/1t2H6oI+EyLXYXDp7s\nhpBkmxPEYBnMObupqWmYoyEIYjAIgoDXPqjH5k9OoqvPl5YYSFAPA21trXjnnS0pLXvXXf+NkpLS\nYY6IUPLpgTb8+pW94Hh+4IUV9HsCaOyI7vLmD3JY+9tPsfHduoTfe/n9Ovzq5b0IBLlTijfd1Lf2\nwesPDbgczwv48R+3Y8OWI1Hv7zhsxwd7WvHo33aj25n8RNfd7wcAcFxisfyHfx3EYy/twQPPf4Zt\nB9sHvR9TwekOoKH91Dr6EWOPwZ6zy8vLhzkigiAGQ0ePF/2eIABgT50jLTGM+dbjo5HHH38Ehw7V\nYtWqpbjggovQ1taKJ574LX7+8/+D3d4Jr9eLr3/921ixYhXuuOPb+MEPfoT3338XbrcLjY0NaGlp\nxp13/jeWL1+R7qGMGf796Um4vEGs+eL0AZf99EA7Dp7sQW9/AHlZxpR/46V367DjSCd+fedKGPXi\nPx2PLwRfgEuaffX6Q+B4AUGOh143ejpDpUJHjwcP/XknLj2rElesnpJ0WbcviC6nD40d0XYNl1c8\nyTn6fHjspT34xZ2rEq6jxykK6tgst5J+TwBaDYMWhxt/2HwQTR0uXHXOtFSHlBJ//FctjjX34cnv\nr4JuFHXzIoaHwZ6zH3zwp9i0aTOdswlilHC0qVf+e2+dA19cXDbiMQxrhnr9+vW45pprsGbNGuzb\nty/qsxdffBHXXHMNrr32Wjz00EMAgE2bNuELX/gCbrzxRtx444343e9+N5zhDRvXXnsjFixYhFtu\n+SZCoSB++9tn4Xa7sGzZmXjqqT/g//7v53juud/Hfa+zswOPPfYb3HXXWmzevCkNkSfntQ/q8dbn\nI/eos9flhz+QWlb3o32t+HhfW0rLSuI3EBpcxrjV4UYwxEfFxIWFX7J1SdnWZFnX0YokcFOxa0jC\n2eUNqL6/uMqG9m4P7v99DXpdfvXfC2eoQ3zibRUM8SjKNeHn31kOPatB7YloO43LG8QHe1oQDJ1a\n5rrPHcDBhh4EQjz8wdOf/SZGH+P1nE0QE4VjzaKgNhtZHG7sSemp6ulm2DLUn332GRoaGrBx40bU\n19dj3bp12LhxIwDA5XLhueeew1tvvQWWZfH1r38de/bsAQBcfPHFuOeee05bHC+/V4fPD3fGva/V\nMqcscJZWF+Dqc1PLiM2cORsAYLVm4tChWmzevAkMo4HT2Re37Lx5CwAABQUFcLlG16SsQJDD6zUN\nAABblhELq2zD+nu8IOD+5z7DzEk5+O5X5wy4vNcf791Vg+N5dPWJoi0wSLHUFbYrcAqxJ/0dSCLe\npGW4JCJRjc5eL3qcPsyoyBnU904n0knJ6Q4MsCTkx239YQEt4Q6/vvHCGciy6PHerha9coEKAAAg\nAElEQVT87M87cNfX5qO8wBK1bE+/uI2T7ctgiIeO1aAgOwOFuSa093ggCAIYhgEAvLezGf/4+AQ6\ne7ynlLnefdQOyZ7ND3KfEUOHztkEQQyWY019MBlYnLOoFP/+tAG1J7qxpLpgRGMYtgx1TU0Nzjvv\nPADA1KlT0dfXJ59wdDoddDodPB4PQqEQvF4vsrKyhiuUtKLT6QAAb7/9JpxOJ55++lmsX/+Y6rJa\nbeTR8mibcNXZE8lQPvf6IThSyFimiscXQn1r9MWK4wS4vMGEmUwlgiDA4wshGOKjtpvbF8RDG3bg\nUEOP/F630y9PIPQPwtPsD3ByplWZPZX+TuaPljy+XAqCX8lvN+3HYy/tgS8w8nfaEh5JUHsGFtTS\n9gkE+ajt0e8NgoGYObj+/CrcdPFMdDv9+PlfduLAia6odUQ81OrbShAEWVADQGGuCYEgj15XJL5m\nu3ieefOzRpxocyaNuaffj5/+6fOoSaM7jkTE3GBvgoixz3g5ZxPERKHX5UdnrxfTyrKwcLqY7Nub\nBh/1sGWoHQ4HZs+eLb/Ozc2F3W6HxWKBwWDA7bffjvPOOw8GgwGXXHIJJk+ejN27d+Ozzz7DN77x\nDYRCIdxzzz2YNWvWkOK4+txpqpkJm80Ku314Jh1pNBpwXLTA6u3tRXFxCTQaDT744D0Eg8EE3x6d\ntHd7AACTi6040daPZzbX4t7rF4HVDv2e7O8fHsd7u5vx2G0rkGM1AIiI0FBMRsrpCeD9d45i+Uyb\n7GP2BzlZJHO8AFYrZiob2/tR3+LEttp2zJwkZnmV1oXBWD66FJPplGJP+juZvUAaw2DEWWNHPxrD\npeM6e7yoKLSm/F0lHT0eGPUsssz6U/q+lKHuTyFD7VJkpl3eIHLDfnG3NwiTkYVWIx4rV32xCiad\nBs/++xCe+/ch/Op7K+XvSZaPRNsqxAkQAOjDgroo1wQAaO9yy8dOa5cHWg0Djhfw/BuH8MAtSxMe\npyfbnWjo6Mef3zyCn33rDPgCHA43RLx4g70JIoYOnbMJghgMx5rFhNz0sixMKrIiy6zH3vou8LwA\njYYZsThGbFKi8u7d5XLh97//Pd58801YLBbcfPPNOHz4MObPn4/c3FycffbZ2L17N+655x7861//\nSrrenBwT2FOcNGSznZpIGYjFi+fioYeOYsqUSlgsRthsVlxxxZfx3e9+F8eOHcKVV16JkpJibNz4\nAvR6Fjk5ZpjNBnnZnh4z9Hp2yPGl8v1Whwu27IwBJ165wv7kGy6ahY/2tmDrzmbUHLLjikE8Ug8E\nOTz96l5ceGYlZk7OjcTQ7YEgAIYMvRyzK5wRZRgmahyff3oCG/5zCMX5ZqxaIFZH6eqLiOSsbBNM\nRjHD1OAQbwLsfT55Hb76SEbUmGFIeRs3dSl+I8skf6/XJwpOjhcSrytsRcjKNqX8e7vqInH6+FM/\nVtf+9hOUF1rxs1tPbbKUJiyKXd4gcvMs0CY5OfFM5DOdMbIvPf4QMi3R2/qS1dOw61gXPjvYDoPJ\ngEyzHh5fEL6wP13LalXHLNlHzCZxfdMn5QKfnoQ7yMNmsyLE8ejo9mBaeTamlGThPzUn8e9tjVg+\ntxgAUFGUiUzFzYW5VRRonb1efH7MgQw9C14QT8I8L4T3mSUujoEYrnMLMTxMmjQZR44cRnFxCbKz\nswEAZ599Lu699wc4ePAALrnkMhQUFOBPf/pjmiMlCCIWaUJiVXk2NAyD+dPy8eHeVtS39mF6WfaI\nxTFsgrqgoAAORyTl3tnZCZtNTMXX19ejvLwcubmiqFqyZAkOHDiAr33ta5g6dSoAYOHCheju7gbH\ncVGP1WLp6fGcUnzDme0AdHjllciNgN3eD4MhC88//1f5veXLzwEAXHPNzQCANWtukZfNySnG44//\ndkjxDTQ+rz+Eje/V4cO9rbhwWcWA/sL6JtE2YdQC5y8qxdadzahv6h5UjIdOduO9HU3weAP4tkV8\neiEIAhrbxcfydocLJlYUZZJn1x8IRf1GVzhT3trhhN2eCUCsSyzR1u6UBZPdIb7f0N6Pzk4nGIbB\nccVMYEeXK+X465silgBlnI4uNwBxeyZaVyBs2bA7XDCmkNDPzjHjvR2RyZ91Dd2oKh68QBMEAT39\nfmTo2VM+lhzh7c0LwMmmbmSaEme6OxyR/dDU2gurXgNBEOB0B5BjNcgxSMdmtlm88Tl4rBNTS7PQ\n4nDL33d7Aqox94UtQALPw27vh1knbtBjDT1YMj0frQ63eHOTZcSlZ1Zge20bNn90HJvDxf4ri6y4\n/5al8vq6eiK/+dc3D6Mk3wwAmDkpB7Unuv8/e28eJkd5XwufWnqfnpnumZ5FM5JGo30DbSAhsYMA\nY3CITYyIwdghcUx8kxsHEzvy/UL4rsF2HpMnjq9zv4TYcUyIINjCZjEIMIjFGpCEFrSNRppNs0/3\nLD29d23fH1VvLV1V3TPDjNY6z6NHPd3V1W8tXX3qvOd3fojGknBhalP5U7m2OMT7/EAoFMLOna8Y\nnquvn4P/+I9n1b9vueVTAIAvf/lPEIkE8eCDf6q+1ty8CP/n//zr2RmsAwcODDjVMw6WodFUJ3OC\nNQqhPnQ6dlYJ9ax5qLds2YJdu+Rcz2PHjqGmpgZlZbLS09DQgPb2dmSz8jT60aNH0dTUhKeeegov\nv/wyAKCtrQ3hcLgomXYwPZw8M4b/5ycf4t3D/QCAWIlsYAAYGs2ApihEKn1wK2r2VFMUhhQfdl9U\nIzGJDIeUTuUlII8Li9OIFYS8B5ALEgn0Y+KU92ZyvOqxNVo+Jj9+fX4yL5otH8XWpVk+Jvd5+08M\nIZnhsFJR8YdGp3fTKIgSJGnqaSZ66CulSxUmJtNGywcAZPMCBFFCmc9lWr5WsWsMKTfFpCARsLda\nkOOreah9hnX0K6R8TlUAPg+LR+5di89sacKdm5sQ8LKIF2wDOb8aqgNIZXmc6o1jfm0QdSF5bI6H\n2oEDBw7OLp5/+zR+9MuPizZMI0hnefREk2iuD6q/C8ubQnCzNA6fHinx7pnFrCnU69atw8qVK7Ft\n2zZQFIVHH30UO3fuRDAYxNatW/Hggw/ii1/8IhiGwdq1a7FhwwY0NjbikUcewbPPPgue59U4PQcz\ni397+QTGE3ncvmk+fvNB96TiZYbG0qiu9IJlaPWknTqhlknPwEgKgiiCoWkMjmhkUbAgqoUeavJ3\nOquRt3ROe8zpiJh+fP2Kx1ZPqKdSlKjvvKRPGiBFiRwnGpIm9NCKEidHzn677wwA4K5rFuB456ih\nIHQq4CdB9j9uH8Eb+87gzz93mWVGtv7cSKTyQJFwF326B0n8IMTailDXhWQyPDgqbx+J6JPHbr2v\nyLYQD3XA60LQ71I9/v3KjAFRmmtDftx1jZyfvf/ksDouAnJMbrlyLn79fidGJ3LYsCyCiZS83Gw0\njXHgwIEDB9bgBRFvHehDjhNw4GS0ZFJHe38ckgQsnqsp0R4XgxVNYRw6HcPwWBo1ikAy25hVD/U3\nvvENw9/Lli1TH2/btg3btm0zvF5XV4enn356NofkAEAik8fc2jLcff1CvL7vDNLZ4oQ6leWQSHNY\nUC9PpxBCbUd67DCkECdekDA8lkF9VQADI5parSecqkJdQGgISdQr1PrxGxRqPaGOpbCyKWxUqKdL\nqPVKujJmUZIMBZF6TCU2byKdx/4TQ5hXU4aFcyoQLvdgeJqJKmT7i8UDHjodw7GuMQyOpi0LH/Xq\n/0S6eFGWvigxlSlNqIlCPawq1DpCbUNkyTaxrDa5Vhf2o71vArwgagp1tfkCyjK0iSCT88nvYXH/\nLUvxq/c6sXlVPd5QLDcXYnb4ZPDEE0/g8OHDoCgK27dvx2WXXaa+9swzz+DFF18ETdNYtWoVvv3t\nb4PjOHzrW99Cf38/GIbBd7/7XcydOxetra34u7/7OwDA0qVL8dhjj52jLXLgwMHFgK6BhCp2/fp3\nnVi3NALaQqgiIPnThdaONYurceh0DIdPj2DrFWeHUDutxy9BCIIEViku83nYkgo1IcK1yl0eSUzg\npmgl0BNDYvsYMCjUZuW3kNAIqkKtt3yUJtQDsRTSWdle4nHLSuxUcqhHdOqpQUnXPbZbn2r5mERi\nxL4TwxBECVuUIrqakB9jidyU1PTCzy12nHhlH9mRfYPlo0R0nt7ykZgEoa4MeuBiafX8Gk2UVqg5\nVaHW1PTasB+iJCE6nkF/LA2Pi0G43NwBk2UocLz1jAfD0Lh8UTUe/fIVCAU9avHlxWj50PcIePzx\nxw0zgaRHwDPPPIMdO3agvb0dhw4dwssvv4zy8nLs2LEDX/3qV/Hkk08CAB5//HFs374dzz77LJLJ\nJN55551ztVkOHDi4CHCiW65XCpd70BdNYb9FJr0ebT1xUAAWNRijl1cplsljXaMW75odOIT6EoOk\nKKnMlAi1THrrFL8qUWGnYvkQJVmVJjeaJCt4cLSU5cNGoc7oLR96Qi1YPu6PpRAdl1XmBsUOMFlv\nsSCKBvXUoKTrHtsRV9XyMQlyRmwxS5Tpq1rFFhGdhu2DjIcXJNsGJWR/2imxBstHCUKdyHAIeOVJ\nr8ko1DRFoTbkUxuzkH3M0FQRD7W8TS6dQl2vKN39sRQGR9Oor/JbKhosQ0MQjFnlZPsLZxYuZkI9\nnR4BLS0t2Lp1KwBg8+bNOHDgAPL5PPr6+lR1+4YbbkBLS8u52SgHDhxcFGg9IyvOD/3eKtAUhRd/\n12X7+8XxIjr6JzC3pgx+r9FwES73Yk51AK1nxqbdNXeqOGuxeQ7ODxCCwCgqs9/DYjxRvHkKIb01\nCnGhKAouljb4lUthdCILXhCxZG4l2nrG1USHkpaPQg+18ryt5UPvodY97oulVLtHQ3UAHf0Tk1ao\n48m8oTjCqngSAHI2X1qyXcXaaRNklG0hFwfi/Roay6Cxxj6+LRbP4KOTUdxyxVzVx83p9l2eF9Tc\nbj3IPrLzCqdzPCgAEooXJfKCiEyOx8I55Wjvn5iUQg3Isx690RQmUnmMJbLweRi4GHrSHmpAs44c\n6RgFL4iqf7oQLENDgnxzxyj7SCXUtFFb0Aj1xeehnk6PgFgspqYy0TQNiqIQi8VQXl6urqeqqgrR\naLTk55+PUafTgTMWazhjsYYzFmvox8LxAtr74miqL8emNY24oXUYv93Xg7b+BK5Z22B4nyhK+MVb\np8ALIi5bHLHcpg0ravHiux2IpfK4bFHp7s6fdL84hHqWsHv3b3H99TdNevlDhw5g/vwmhELh0gt/\nAqiEWqdQ53kRvCDaNr8gqmmdztjvYmjT9HkxkISPZfMq0TucRG80BY4XEBu38SaLmk1CX+xHCJC+\nELGU5aOyzI3xZB4d/XI8X4OSKzxZD3VM8U973AxySmqFNk7d51msj8wIAJNrY03Udo1Qywr18Hjx\npI+XfteF9z4ewLJ5Icyvky8KvG5f5HkRXovEO7KMHYHN5nmEyz0YmciphXpWIIp0uNyLnmhStX+Q\n/+0IdY2a0pHBWCKHUNCLTI63bT1u5aEmhPrQKZnMFSPUAMDzEhhlX5Bjo18foN1wXqweaj0m0yOg\n2HuKPWeF8zPqdGrX7EgkiDfeeOesXLMnM5bZ3C9TgTMWazhjsUbhWE6eGUOeF7GooRzRaAJb1zXg\n7f29ePrV41gyJ6g2aRkez+Cnr5xAW884ynwuXLk0YrlNzUpd0O8O9qG+wmwDLDaWYsvZwbF8zAIG\nBvrx5pu7pvSeV155EWNjs+/1IQSBEGq/RyZu6SK2j6HRDFwsjVC5R32OnaJCPayo3LUhPxojAQyP\npdEznIKkG4tVHB1RFAufNyjUJQj1fOVLdbhdzkVviMiEy05RLgSJzKut9BnGID/Wq8Dm9dmp2XZI\nZXlQlHyjA2iWD+IztgNpsa1vU27YF3b+brVg0vy6KEnI5gSEy71gaKqo5YMo0mV+F8p8LlWZTmaL\nE2pyk3ZmKIFUlkco6AHLULb7irNQqGsqfaAorWhyTpUdoTafZ6UsH5O5CbrQMNkeAW63W+0RUFNT\no6rPHMdBkiREIhGMj2u57kNDQ6ipKV6Rf77ifL5mO3BwqeBEt9zvgnQ2rgn5sXl1HQZG0njk/+7B\nd36+H//0i4/x6E/2oq1nHOuXRPCdP95oO3u7dG4lWIbCsc6z8z11FOpZwD/8w/dx4sQx/PSn/4qO\njtNIJBIQBAF/+ZePYNGixfjP//wZ3nnnbdA0jS1brsHy5Svw3nu70dnZge985+9RV1c35c8URQmg\nULQaFtCIE1HgfIoSmsnxlk07JEnC4FgatSGfYd0uhgY/haJEolDXhH1oiJShrTeOA23yD3RdlR99\n0ZSBnOqJDC9IIOI5UVJzeUFV1UulfMyvC+Jw+4haAKl6qCepUJO245GQD2eGkwY1V0/8rNZnTAQp\nTeDTWR5+D6vu64hC4oeLqHrZPK9aaOwsL3Z+cbUo0UKJzeYESJBvusoD7qJFiUSJDvpkQk1uANTO\nhnaWD0VdJr65UNCD0YkssnlrNZxsk95D7WJpVFd4VY+8VcIHoJ3z+uPHK7Ms9paPi49Qb9myBT/6\n0Y+wbdu2oj0CvF4vjh49iuuuuw4ejwevvfYarrnmGrz99tvYuHEjXC4XmpubsX//fmzYsAGvv/46\n7r///nO8ddPDVK/ZmzZt+MTXbAcOHBjR2j0GipKJMMHvX9OM8WQO/bEUugcTEEQJAS+LL962AptW\n1FpG1RJ43AwWN1biRPcYJtL5oo3JZgIOoZ4F3Hvv/di5879B0zQ2btyMO++8C52dHfjhD3+Af/zH\nf8azz/4nfvWr18AwDH71q1/iiis2YdGiJfirv/rraV2Ys3kef/1/W3DT+kb83tULii6rTnEzRoXa\nrjAxnsojlxdU4kPgYmlDFrT1+3jVA0yylGtDflUh3n9Srt5tjJTJhNpGzRUEEVAykvVWgHSWR3nA\nbRi7/vVChRoAyv1ybjEwFUIte8yJ/cKY8qEvSrRQqAXrZe2QyXEG8ul2MQgFi0fndQ8mQER8u5QT\n+wQSxfJhMTayX31eFkG/S70psoLeKx30uXCGS4LjBTX32dZDrZxXJ8/IykQ46EEXXcRDrRwzd4EH\nty4cQHQ8q5Brn+V7XUSh1u0XXr3BtFao7eL7LmRMp0eAIAjYs2cP7r33Xrjdbnzve98DAGzfvh1/\n+7d/C1EUcfnll2Pz5s3neOumh6les7ds2fKJrtkOHDgwIscJaO+fwLzaIPxe7fciFPTgrz6/BoA8\na5rMcPC6GMu+CVZYuSCME91jON41ik0rZve7etET6p2nX8bB4SOm5xnaflq5FNbWrMZnF91Rcrkj\nRz7G+PgYdu36DQAgl5MVtOuvvwl/+Zd/hq1bb8Mtt9w2rTHoMZHmkMxwONY1WppQF1g+iLXALota\nS/gwE2quiL/0p6+cwOm+OJ782mZ43SyGxtIIeFmU+VyqQkxIdmMkgA9hHZtX+Fi/TCrLoTzgLmn5\n0OcrRyp9YGhaiVCbHFkiGdQ1xPJhozrnLEir3djtkM7xqCuwLNSGfGg9M448J1heRDoHNN+Xfpus\nbi4KoRYlWqjnGcU+4vOwKPe7cWYoiRwnwGMxBr3lg9wQJDM8UhkOHjdjUJT1KPe74HUzqoVHs3wU\nH2+h57k27MORDjnxg/jsCqEq1JaWD2sP9cVo+QCm3iOAZE8XYtGiRfiv//qvGR3bpXDNduDAgRGn\n++IQRAnL54Vsl6Epasoq88qmMH6BdhzrdAj1BQ2Xi8XXv/4IVq26zPD8N77xN+ju7sJbb72BP//z\nP8W//ut/FF1PdDyD7sEE1i+NWE5vEMVtIJay7dZHoFo+lCluXwmFWk34CBlVP5ahixLSeDKHTI7H\nwVMxbFxei+h4BnNrZGJLigIB2QsbKeFNNnQmLFCogQIPtYXlocznQnWFF7F4Vv0sN8tYEmArjE5k\n4fewCCh3zVZpJIB1bJ5xO4p/nihKyOQEkz2iJuRH65lxRMczhn1H0KH4p+Ux2CjUdpYPtS16EYXa\nzSKoXMQSqTw8lWYFOKnYQYI+N4I+Zdl0HskshzKvtToNyIkxtSE/uofkm4JQ0KvE29l4qDmzhxrQ\nbvjsChIBfVGi+TwzEWpi+bgEihIdaJjsNfuFF3aeoxE6cHBxolXxTy+bb0+op4O5tWUI+l041jla\nkh99Ulz0hPqzi+6wVCZms9KVpmkIgoAVK1bh3Xd3Y9Wqy9DZ2YEPP9yDO+64C88/vwNf/vKf4Mtf\n/hMcOnQQ6XRKfY8emRyP33zQjV17e8ALIr71hXVqPrEe+u6BibSs2tpBi82bXFEimea3Uqh5wb7d\nNlFmPzw+hEUNFeAFCbVKokOZz4WKMjfiyTzqwn6VzNilZ+hJtN4KQFTNTAkPNctSmFMdQCyeRTUh\n1C56UjnUkiQhNpFFTaXPcpx6BdqyKFE/9hLqGlGEAwUElBQmDo9ZE+ouG0Kt32+lLB9FCbWHgSDK\nY5pIc+o+1COhs3wEfPI5lczIMyf1NkWCBLVhn0qow7qiRFGSTDUBVh5qAGoXz+Y55bCDWpSozw6/\nBHOoz3dcCNfsZDJpec124MDB9NDaPQaaorC4saL0wlMATVFY0RTGh8eH0B9LWf6GzthnzdqaL2HM\nn78AJ0+2Ynx8DH19PfizP/tjfP/738GaNetQVlaG8fEx/MmffBF/8RdfxcqVq1BeXoE1a9bhf/2v\nb6Kjox2AbDPY/tQHeKWlW42jGrIpTNP/6Otzna3A21g+MiUsH1Yeanl9NkRNIXbHOkfR3h8HoFkm\nAKBRURLrqvyW5MVAWg3KtfY4leXACyLyvKgqloWEmqEpMDStJj9EKuXoHLeLmZSHOpXlkcsLqCr3\nqjchVg1ogNJFiaXsA0Rxt1KoAVh6mCdSecTiWXUfTl2htm6gA+gi/JSiRMC+WyLxUAf9LlXNHkvk\nkOdEW/80Qa0ujjFU7ikaWZdXFWqj7WRBfTn+9x9vxA3rGkzvIWAtLB92CjXtEOpLClO9ZldWVpqu\n2Q4cOJgaJElCNs9jaCyNzoEEFtQHVU4yk1jZpHRNnOW0j4teoT4XCIVC2LnzFdvXv/71vzY990d/\n9BX80R99Rf27eyiBeDKPTStrccXSGvxo5xGMTVg3YDG02B5JY2kRD1Kh5cPvkYmJnUI9kcqDoSkE\nC0iRS20/LsFlcRZxOuXzNy1nABhJeUOkDMe6xlBfFdCI6qQsH9rjdJZXx10ecCMWz5oIJfHablxR\ni9N9caxaUAVAJmT6Vtl2IJF5VUp0nGlsJYoSp+KhJoS6kIBqCrX5horE5c2vC8rNagydIkt7qEul\nfADyTRfxbidsmruQfRlQUj4AzS5UklCHtVkDv4fVCgIF0aRE23moAS29xQ5Wlg/72DzzbISDixcz\ncc124MDB5PH06yfx3uF+w2/6TNs9CFYqbciPdo3ilivnzcpnAI5Cfd6C/NAvbqhQiehoImu5rJ6I\n9pdQqNWiRGL5UOwFmZy1gpnjRLhdtMnWQYiOXRY1L4gIeFlQ0NqM65VIEouzuLHCkrwYLB82j1NZ\nTlXWKxQFtTAqjijX8+uC2H7/eoSCcpa2Z5KWD1KQWFXh1SU/WJP9UpaPUh5qcnNQqFBHQlrzk0IQ\nQk2sQPaWD+tt5SbjofZoHmo7hTqR4eB20fC4mGkQavm8CAW9oCjK0lqjjldN+Zj6pUvLoTbPfjCm\nosSLt1OiAwcOHJxLpLM83j3UD5+HxermKmxaWYtbrpiLm9c3zsrnhYIeNEQCaO0eKxpB+0nhEOrz\nFKr/l6ERVhqqjNoo1LzB8lH8ZDF3SpSVR7uiRI4XTNPrZFzkdcsxCSLC5V6D55sokQCwZnE1nvza\nFqxoCk/C8qHPDTYWJeoVank8RlXWLl3C7WLAC1JJwkQyqMN6K4INwS+ZQz1Ny4fHxSDgZS1bf5OC\nxCWNCqG2SfawIvvA5CwfPg+L8oDiobbplphMc+oshkqoRyZHqOvCflAAqpXzXPM6m8dEtsPuuBaD\npUItSmBoyuTVZp2iRAcOHDiYFRzpGIEgSvjMtQvx9c9fjq/cuRLbblqMijJP6TdPE3dubgIvSPiv\nN09NuqvrVOEQ6vMU+uIrr5uF38NiNGFDqHXEY7CkQm2MCfOVKErM87JCXQiXhWfZOCYJLENj48pa\nAEDAyxqK7SiKUtVizfJhrfwaCvsECYT7pDKcOm6iUOvJEieIqjWlEEThtCvWI4grJLayzFPS8mGt\nUE+FUCu2CYtUDJahTUWNkiShayCB6gqvetPFFWx/sbFJkqRZPkoo1CSqyK5bYjLDoUxJ9yA530RR\nL0WoA14XHrprFT5/42IAmt3CilBbdUqcLFjLxi6iKYMacDzUDhzMJDI53tDF1cGlDdLUbdOq+rP2\nmVcsq8GKphA+bh/BwVOx0m+YBhxCfZ5Cr1ADskJK/LyF0BOEkYlc0QuXWaEuHpuX56wV6mKEmhA1\nF0Nhw9IasAyNxiKVtaRLHW/TMMVAgERRtR+ksrxq+bBSqPkSCjVgr9wSkMYkQb/LmlDrEyOsYvMs\nCuDsQG4OyvxWhJoyWUai8SySGQ7Nc8otj4fxsXlsoiSBjMhKqc+oHmqmqOUjzwnIcYI6bqKwE0Jc\nilADwIZlNZirtI9lLW6wCrdjegq12cbBC5KpSyLgpHw4cDBTkCQJjz/9EZ589tC5HoqD8wAcL+Lj\njhFEKr2YXxcs/YYZAkVR+MLWJWBoCv/1Zhty+ZlP6HEI9XkKvmBqO1zuRTYvWDZgIcSFqHbEu2q5\n3oLYPJah4XbR01aorTraCaJM1BiGRpnPhb+5bx2+/OnltmOytnxYe4EFQUJFmRsUZEXXZPkosDzY\nE2qiUBf/UhFFNuh3W1o+BIPlw6IoseBmoBjsLB+AvC8LyV2PEjU3vy6oKxKdfGweabtdOE6CjC7l\nw8XS8HmsbSdqwocybo+LMSjIkyHUemhKsr1CXZjKMZX16veRIIqWBY5Wx9qBA0FL6z8AACAASURB\nVAdTR180hf5YCu39E2pNioNLFye6R5HLC1i3xLqvxmyiviqA2zbOw+hEDi/u6Zzx9c8qoX7iiSdw\nzz33YNu2bfj4448Nrz3zzDO45557cO+99+Lxxx8HAHAch4cffhj33nsv7rvvPvT09Mzm8M5rFObt\nhhV7hFVhIiEepKX3QMyeUGudErVD7/OwlrF5oiSB48Upe6j5grEvqC83ROYVopTlw0BKBREuloHf\nyyKV41USWlGgUEuShDwvL2sFVaEuQaiTGQ40RcHvZVVfrV1yh6XlQ0fISsbm5axTPgDF8lFAMLPK\nHXbA6yqpUFuNjSu4USkEIdRetzyLUe53YcIiGSWp65JIYHg8RULNWORFE+SVm6TpXIg1hdp4PhUm\nfABOYxcHDgCgP5bCj375sW1k62RwpGPE8rGDSxMH2mS7xdrFkXPy+XdsbkJVuRev7+0pGTM8Vcwa\nod67dy+6u7vx3HPP4fHHH1dJMwAkk0n85Cc/wTPPPIMdO3agvb0dhw4dwssvv4zy8nLs2LEDX/3q\nV/Hkk0/O1vDOexQqcaFyOT/ZqjCR/OiTKfOBUfuTRIvN00iE38NaKtRqVzqLVtPFUj4IEZqsiqgp\n1NZNUAqn6F0MDb+XNRQllvlcoCmtnTgZg51C7WEnb/ko87GgKaok8bcsSpySh9q6sQsgF8kVEkyy\nPpah1BsH29bjFmMzKP+Wlg8eHjej+onLA24k0nmIBQUdqi1GR5z13RFJo5fJwiovmoATxGn5pw3r\nLfDkO5YPBw6s8eoH3Th4KoZ/ffHYtGdrPm53CLUDGaIo4dCpKMr9LixqmNkGLpOFx8XgvluWQJQk\n9EUvEELd0tKCm2++GQCwcOFCxONxJJNyfJrL5YLL5UI6nQbP88hkMqioqEBLSwu2bt0KANi8eTMO\nHDgwW8M771Go8hZTqAmpJa29J6VQM0ZCncnxpsrXHG8fUWZlMSgcu5XyZ4VSsXlkzJIkQRDkKXq/\n14VUhjMUzrlYrR06+d+uKNHFTt7yUab4h7VxWnu9rci5XcSeFTI2sXmAfLwKPdS8rsDU6gZHf2xy\nVmPjrbdDHU+eVztpAkC53w1JkotB9UhkZBsI2U/yY20bSCvyyaKoh5qztmhMbr3WOdRWRYmEUJea\nVXDg4GJFNs9j/0m5eKxzIIFX9nRPeR3pLI/TfXEsqA+iNuTD8e4x22ZgDi5+tPfHMZHmsGZxtSrU\nnAtcvqga//Q/r8H6pTOrks8aoY7FYgiFtJDucDiMaFT+cno8Hnzta1/DzTffjBtuuAGXX345FixY\ngFgshnBYDuCmaXlaN5+3ThW42FFICMOKQm3V3IUQj6pyL3wepmgWNW+hUPs8LARRMpFjTaGemoea\nL0FmC2Gl/IoF0/KAVkTHMhQCXhZ5XlQ9vX6vQqiVZe1aVBOQbcoVSfkQRBHpLK8qr4yFZUAwEFgr\nhbq4CqxHOsuBAiw7RTEMbdrXvM6+Q0ioXok22D+sFGqbbpQEmZxgGEtQ7ZZoJNRJK4VaeUw8+lMB\nUYytcrs/mUJtlUMtWc6kOB5qB5c6PjoZRY4TcPP6RoSCHry0p0vNvZ8sjneNQhAlrG6uwurmKuTy\nAk71xmdpxA7Od5B0j3VLzo3dQ4+A1zXjHu6z1ilRr34mk0n8y7/8C1577TWUlZXhgQceQGtra9H3\n2CEU8oO18cmWQiRy9ipMi6G9dxwURaFZNwXCKNtUWxNEJFIGTjnwaU4wjdujTK9XVQUwr7Ycp3vH\nwQui5fb5/bLSHar0q69XKmTdV+ZViTsAZBUuUR70mtYVVpq0eH1u02s55bCVlXkmtY/dioLJuhh1\nebdHI2c+v/wZJL2EZWgldWIMcaVocG5DCB43A1GSEIkEITGySh+0GYM6fr95/ATjiRwkANUheV/5\nFGWWZbVxkuPkcTMQJPM55Q+MqY9ZF1t0f+QFCX6fCzRNmdfjdUGUJISrytSbIa9y3KurAqipKYeb\npSFR2nspPVG0WGdKV5ToKhibJEnI5Hg01pSpz9dVy5YiWnecAECk5M9prK9Qn4+EZT9/ecCNmppy\n07YW2w/lyjkYsDjveEFEMGB+fjKoUmL83B6X+n5BlODzmo+LoJD6UsfMDufLtcWBAwK5XmDyN6N7\njg4CAG7a0Ig1i6vxg2cP4d9ePo5Hv3SFpQ3QCsTisXphFdJZHm9+1IsjHSNYPksd8Rycv5AkCQfb\nYvC4mYv2+M8aoa6pqUEspmX9DQ8PIxKR70ra29sxd+5cVY3esGEDjh49ipqaGkSjUSxbtgwcx0GS\nJLjdxaeLx6ZZLBGJBBGNJqb13pnGd/9jHxiawnf+eKP6XDIlK9GJiQyikABF/ewfTprGHZ+QiUIq\nkUV1hQcnz0gYHEnBY3HzNR6Xl02ncup6yCW2t38cQk5r3zw0LL8u8ILpM7MKkR0dS5leG1Y6I/J5\n8/usQLzD6XReXT6Z1JT4sXgG0WhCzWlmGRqssm0DsRQoAKlEBgxFIad8JsnjFi3GDgCcYq+IjZj3\nJ0Gfsh0uhkI0mlBjdtIZTn1PRhmT18Ugk+VM6xob185P/fZZYSKVg88t/1AVLicqau3QUFz1S5Pj\nnkxmEY0mwDK0YQxp5RjRFIWkxWcP6/5OFbye4wQIogSWptTnGSVkr6c/jvoK7cZraIQcb860rN/D\nmj631Hcvp+zTkVHzuZXjBFAW+2cySCnn1EQio76f4wVIomTxnZKtVaWOmRWmcm1xiLeDs4FDp2P4\n8c4j+Ma2NVg6rzSZGYln0do9hkWNFagN+VEb8uPm9Y1486Ne7Hy3A9tuWlxyHZIk4UjHCMp8Liyo\nK1cKymkc6RjB529YNBOb5eAsIZPjIYjSlAvM9egZTmJ4PIMNy2pswwIudMya5WPLli3YtWsXAODY\nsWOoqalBWZmscDU0NKC9vR3ZrPyjdfToUTQ1NWHLli147bXXAABvv/02Nm7caL3yiwyZLGfypRYW\nJbpYBkG/y7K5i74IcE6VTIh7hpKWn6XlUGuH3u+1bu6SU2wCnmJFicU81OwkPdRWVgoLD7W6nYqH\nGpAL4rweuWjQykNt57edTGOXwmI7q3bUxJbgcTM2KR9TK0r0W9g9AJumJGR/KIq1y0UbbR6CBBcr\nWy44y0g/6wJGAMjqvOkEHjUZxbhsMctH2RQLEgH7hA1JksBx07d8FKaHiKIESbIunnWKEh1cTPjo\n5DAEUcKrH56Z1PJ7jg1CAnD1aq3xxueuX4iakA+//agXw+OZkuvoGU5iPJnHquYwaJqC28Vg2bwQ\n+qIp254KDs5P/PD5w3js3/d9Iv/7S7/rAgBctaJ2hkZ1/mHWFOp169Zh5cqV2LZtGyiKwqOPPoqd\nO3ciGAxi69atePDBB/HFL34RDMNg7dq12LBhAwRBwJ49e3DvvffC7Xbje9/73mwN77wCJ0gArP2x\neg9wKOjB4EgakiQZvD/kJGcYChElni46lsaiOnMzFTXlgzF6qAGYovOKdaVji3moC7oxloJlDrVg\n9veqhZoMjYBXO3X9Svt01qoosVRjlyJFiWq+slJgZ9cpkaEpuFlGJeB6TDblQxBFZPOCenNTCMai\nHTfxw6s3XQxtIPUcL0/xuhjKumCySFFi2oJQax7kAkKt7Cd9MSUpSpyOomGXQ03yzafT1AXQPP08\nbzyfLIsSVQ+1Q6gdXPg41SP7lo+0j2BoLI1axfJmBUmSsOfIAFwsjQ1La9TnPS4Gv39NM/7lxWN4\n6f1OPHjHiqKfSewelzVXqc+tbg7jSMcIjnSM4Lo1DQCA1u4xeNwMFtSbrWEOzj3SWQ6neuOQIJ8/\na6fhf27rGcdHbVEsaqjAmsXVMz/I8wSz6qH+xje+Yfh72bJl6uNt27Zh27ZthtcZhsF3v/vd2RzS\neQlBEFH4u00K3PSkNBz04sxQEqksbyAqhLS5dGkPdnFwQoGqCUBVRQsVakI2i8bmWeVQT7UoUSWq\ndp0SFQIkakq8nrz5FL/1VFI+1KLEIrF5pKkLIYcURYGmKAOh5EUJDEPB46Kt94VNE5hCaF0JiyvU\nVjcd2iwGjayO1JMpVhdjPTauCNkn4/EbCDUpFjQum8py8LgZw7lKkj30yR+ThV0ONVHGrXLRJ7de\nEsdnnPGwOkdoynxOXkx44okncPjwYVAUhe3bt+Oyyy4DAAwNDRmu2z09PXj44YfR29uLPXv2AABE\nUUQsFsOuXbtw4403oq6uDgwjH5Mf/OAHqK29eBWoCxFjiRyGxzPweRhkcgLePtBX1LLR3j+BobEM\nNq6oNd3gX7G8Bi+3dGHPsUHcftV81FcFrFcCmXxRAFYuCKvPrW6uAnAKRzpGcdXKOjz721PYfagf\nPg+Dv39os2VkqINzi9N9cVXue+/jgSkTalGS8NxbpwAA99y46Kw3czmbOGtFiQ7swQsSREn+R37I\nNcuHdvKFy5XovImsgVDzOtVZU45tCHVB63FAI02F7ccnFZtnmcRgjuYrBoqiwNCUkURbWCUE1UpC\nG4geUahdDA1RkiCIYumUD7a0Qq21HddIoRxfZ0z5YGj5RoYXJIiiZIgDssusLgTxh9sp1GpTGX2q\nSIHCqk85AZROkQwFt4u2Uc/N1hUCtamLRyOvViq5/LdkIqWNNWUoD7ixpHHqWaN2OdSc7vhPB6rC\nblKorVI+Ll7Lh75HQHt7O7Zv347nnnsOAFBbW4unn34aAMDzPO6//37ceOONCAQCeOihhwAAL7zw\nAkZGtDzhp556CoGAPbFycG5xqnccAHDblfPw1oE+vPfxAH7/mmZ43NY3pnuODAAAtqyqM71GUxTu\nunoBfvzCUfz6/U589fdWWa4jneVwum8CzXPKDdfP2rAfNZU+HO8axf/++X70RVNqH4Q39vXgrmua\nP+nmOphhnOyRzx+3i8bH7SOIp/JqI7XJYN+JYXQOJHDFshosPEfZ02cLTuvxcwxRlNRGGYX+V5Yx\ndoQL2zR34XV+a0K8rLzNgDWJUC0fOSO5zE+msUsRD/VkFWpAJvjGzGazv1fzilNGy4dXU6jJmMjY\n7Yof7PzAeiQyZm8wy1AmXzSj+AMBIF+gBE/WQ01mB+wUGuumMkbLh5tlDDYOThDBsgzcLGN5nDiD\nfaRQoTZbPlw2VgyrboMVATf+8c+vxqaV5h/lUrBTwkn033Q91K4Col4sL/1i9lAX6xGgxwsvvIBb\nb73VQJZ5nseOHTtw3333nbXxOvhkaFMI0fL5YVy3Zg4yOR4txwYtl93fOoz3jwygssyNFU1hy2XW\nLYlgXm0Z9p0YRm/Uulbn444RiJKE1QurTK+tbq5CNi+gL5rCDesa8L2vXoWg34U39vcglTXf+Ds4\ntzjVEwdNUbhzcxNESULLUetzxwocL+AXu9vBMhTuvn7hLI7y/ICjUJ8FRMczqKrwquqzHnyBokiI\nHqe0WNbDrrmL3gqhKtR2lg8LhdqnqJDpnHVhpCWhnkxjlykQH5Pya2Ft0JrdMCqJ1o9fT6g5oTj5\nIpaPQgKsB7F8GBRqmjaNjWEorciRF+HV3bwLk7R8kKQTu6JExoLMmooSWXlsgiir5jwvwuWn4XLR\nyPOCrfeebIcehFDrx6ONodDvP7U4rlKwUuOB0tnipVA4fvV7c4l1SozFYli5cqX6N+kRQIrGCZ5/\n/nn89Kc/NTz3+uuv4+qrr4bXq6W8PProo+jr68P69evx8MMPl5zSvRiiToELZywdAwm4WRobVs/B\n0oXVeKWlG+8c7sfdW5cajtWuD7rx//36KDxuBn99/xWorbX3NH/pjpX4f3/yIV7d24PtX7rS8Fp1\ndRneOtgPigJu3bzANLbP3bwEw/EsPn31Amy5bA4A4O4bl+DfXz6G948N4b7blk9nF1jiQjlGZxuT\nHUuOE9A1OIHmxgp89qal+PX7XWg5PoT7Pr3C9nue4wQMjqQwGEvhw2ODGJnI4q7rFmLF4hrL5S/E\n/WIHh1DPMroHE3jsZ/vwxVuX4vq1DabX9eSksG20iVDbKdQ6pY2QAysrBmDdKVErSixUqItYPopY\nS6ZalAgQomrjoVYtHzqF2qe3fCgKtY7klyxKnILlQ2+vYWgKhc1aWFrX+psrVG8na/lQFGFby4dV\nyodxtkF/Q8G4ZfuHi6XgYWlIkmLNYPWE2jpVBbBWqNkilg+/Z+ZikKxuHgDoZh2mq1AbLR9CkRs/\n4pe/FBq7WOX9Hzx4EM3NzSaS/ctf/hKPPfaY+vdf/MVf4JprrkFFRQW+9rWvYdeuXbjtttuKft7F\nEHV6oYwlneXQPTCBJXMrMT4mR4muXxrB3hPDeP+jHiybHwLHC3hzfy+e392OMp8LX//85air8BTd\nvvnVfjTPKUfLkQF8eLgPzXPK1bG8s/8MTveMY/3SCDyUOeLSSwNf/wPZs09eu3JJNX7pd+HFd9ux\nZUXtJ4pnm8x+Odu4UMdy8swYeEFCc10QuXQOaxZVYf/JKPZ+3K8ecz06Bybw3f88YLh2l/tduGnt\nHMvPvBD3SzHS7Vg+ZhlRJV5ozCLuDjD6RPVqKfG/6mGrUOstHyUVaoWETSE2zzLlo6hCbV/sZYdC\nD7W15UOf8qFTqJXxk23nBFErjCylUBexfCQzHHwexrAOhjF7vRldN0CT5WOSKR9pC0VYD6uEDf0N\nBmC8oZAkSVaoGVoj+wVj0x+7QrJvnfJhbcUQLCwfnwR2rcdnTKFWLR/mAl3j8tRF2Xq8WI8Agt27\nd+Oqq64yPJdOpzE4OIjGxkb1ubvuugtVVVVgWRbXXnst2traZnfwDqYEks6weG6l+txN6+Xj98Nf\nfoyHnnwHf/qDd/D87naEgh586wvrJpW2QVEU7r5OnsL/91dPGAjUK3u6AACfvmr+pMfpcTO4beN8\nZHIC3tjXM+n3OZhdEP/04kb5/LlamVF4X/HZF+Ldw/3gBRFXLq/B3dcvxEN3rcJjD268ZIpNHUI9\nyyBKX2GBFYHB81qgUBdOi1YGPaBgoVCLGrFSW1CXsHzoCZBdUWJRy0cRDzV5brJFiWRZe8sHSfnQ\nFEWvm1EtNGT8BstHyZQPeZtyJSwfhUqJmfhrsXmAmaALBYkgdlAtH7axeWYyWzgT4HJp208i5lhW\nT/bN3me7sWXzJHVEV5RYxIoxo5YPu6JE9QZvemp4IVEvNZMiz0ZcfIS6WI8AgiNHjhhSmQCgtbUV\nzc1a0VgikcCDDz6IfF62Ru3btw+LF5du+OHg7KFNKUhcMlcrBlvUUIENSyMIeFnUhn1Y0RTC1ZfV\n42/uW4c51ZMvLl02P4Tr18xBXzSlZgyf7B5F65lxrFwQRlPd1GLwbljbgHK/C29+1KNGcTo4tzhF\nCLVy/qxaEEZlmRsfHh8yze7ygoj9rcOoKHPjK3euxO2b5uOKZTVTKmC80OFYPmYZROnjeesfZj2R\n4QrIddBvJKQsQ6O8zG0KxRd0U/92hWPasmYPtddtF5t3dosSDYqpITavoLGLUqzp97JIZjiVhOoV\n2nwJhVqNF7RRqCVJQiLNYX6dcXqHoWnk8trFXi5KtFeo9bYKsZiHerIKtQUJtlKo9TcUKtkvQqgL\nUz6sFGrV5iMWKtTSjBJqqwJMYAYUapoGRWnrKZZDLS9PXZQe6lI9AgAgGo2iqspYUBaNRtXutgAQ\nDAZx7bXX4p577oHH48GKFStK2j0cnF2c6omDooCFczRCTVEU/uz3V8/I+v/ghkU40jGCV1q6sW5J\nBK8p6vKnN01enSbwuBncunEenn+7HS3HBrF1w9wZGaOD6UEQRZzun0B9lR/lSh0RTVPYvKoev/mg\nG/tPDmPzKq3xz7HOUaSyPG7e0GhIurqU4BDqWUYphVpPZIwpH2YPNSBnUfcMJwwRe7yiktIUpSqZ\n9gq12fJB05SSUWoTm+eapod6KkWJNI2sqJFRKyW2MNUioBBqXzGF2mYMNCUXEtp5qLN5ufV2sFCh\nNqV8iGAYyjb/284XXoiMqlBbT41Z51Abj6WxKFM7Bi7V3mJP9s051GZCbeVtlmMKpZm1fFj4xYFP\n7qEG5P2ozngU5HgXojB55mJCsR4BAPDSSy+Z3nPrrbfi1ltvNTz3wAMP4IEHHpj5ATr4xMhzAjoH\nJjCvNmibb/9J4fOw+NLty/Hks4fw4xeOIBbPYuGcciydV1n6zRbYtKIOz7/djoNtUYdQn2OcGUoi\nlxewZK7xWF63Zg5e+/AMXmnpxqYVdSp5/vD4EABg40XcCbEUHMvHLINM5dt5mvXEl6ibev9rISoC\nbvCChKwu4o4TRC2L2KZwjEBN+SggQD4PayLU+SJT7AxNgUJxD/WUihItLB+FSQv61uOARj5Nlg9B\nn0Ntbw9wu6zj5ABzUxd1nKZ4PwmszvJRWJRIxu5m6eJFiUrCiq1CbWG34AX5porWpXwAZg+522Y2\noZhCrRJqt85DbTGGwpucvuQAxnNx2+2cDAayPQDN2+ZQZ6k4BlND01o3y1Dg+IJcczuFmqGLzio4\ncHA+o3NgAoIoYUnj9MjtZLGyKYzr18xBLC7PnN5+1fxpN+8IBT1YOKccJ3vGHdvHOQaxexSeP5FK\nHzavrsPASBr7WocByPVWB0/FEKn0ovkS7njpEOpZhmr5KEFwAY2M6f2vhdCTJnUdgqiqeqUUat7C\n8gHIhDpt13rcQqGmKLmJjFWayHQtH4XKL7GamIsS5bGTLGrV8jEFhZpsV85GobZq6iKPU1M4JUWd\nlXOoi1s+3C6mqEKdmqaHWk8GteJD3Q2FriixUKG2s9gAciY5q+u8CehbgpvtOCxDIy9w+MFHP8Yz\nrb+w3c5S6Ih34+mOn4Gt6TGRfDLeD1Ov4vv7/mlapJpltDQZruBmoBAXq+XDwaUBkj+t90/PFv7g\nhkWoqfRh0dxKXL7ok7WWXrskAkkCDp2KlV7YwazhZIF/Wo87NjeBpii8tKcLoijh8OkYcpyAjStq\nL+pOiKXgEOpZhmr5sFEnrTrfFSuos2r5zQuSSr5Le6jNlg9AUajzvCFCq5hCTT6rWFEiy06hKNEi\nNo8NDYLyT6j7TiuoVCwfvgKFmmz7ZAk1y9i2aFebuhQq1DolnTTkyQTOIAtZlTUVJSr72+Mybl8h\n0jkeFAXb7mVWXQp5QTI06NFsJ4K6/QlXLzJMVHneWqG2sjZkcrwpCk8r6jMnsDAMhaH0MPJCHn2J\nfuO6+Cze62sBJ5RWnLri3crG5EzfGbJNGTGJvMjhJ0efQV7IF11fZ7wbL3fswkvKPyo4orMQlbZ8\nXIxFiQ4uDbT1ytekxbOsUAPy78djD16J73/tast+C1PBOqW19cFT0ZkYmoNpQJIknOqNI1zuQXWF\nz/R6TaUPm1fVoT+Wwv6Tw5rdY/mla/cAHA/1rEO1fNgQXL0VhJCxYi2WrYoB9UolTVNy4VWpxi4F\n09x+DwtJkr3DxG+X40VQlP2UuNzq2kw4VA+1RcMMOxRaPnhBBD/3I7gnwhASjcb1Kvvg5vWNiFR6\nURPyqeMBiEIt3wwUU8ndLhrjSTuFWrF8FHioWZqCBLnDpSBIAJtHrKIFx7KjABaYoulUy4eLsWz/\nTZDJ8vB7WNsfIytfsSAWKNS62Qmyr07TuzHMhQGsNZF9si6vmzEp9Zkcb/JdWjV20UckDiiKcTyf\nQIbPwsfKuel7+vdi5+mXMZFL4NPNt9juAwDoTcpxTBQjmHOolX2bF+Vj058axPNtv8YXlv+BaT15\nIY8XO17D7p7fQZ7vkUHVB8C036yMvXhRIu0o1A4uUOTyAk73xuWCsrOUsuBxMZYF7FNFXdiP+io/\njnaOIpcXbEUGB9NDnhPQ2j2G1jNj6BqUc5ddymwkw8i1WLwgIZnhsKnZniDfsXk+9hwdxAvvdiAW\nz6IxUoaGSJnt8pcCHEI9yyipUBtSPmTCUCxD2Y5Q61VkF2NtxQCsOyUCxug8QqTynAA3y9hO4bhY\nGrxF7Nx0ihL1RJWmKQgSB1ASKFbQUj54EWDzaIm9jQW1n8LChgosbNCmowwearIPLewq3RM9ODXe\nAberDHlONHUQBICkheWjI96NZFkbgBoIohxNR9Hy8c1Lct54zkKhpoMj4MJ9EMYaYYd0jjfZPY6P\nnMRgehg3zr3GMoe6sEOh2fIiQaA45JFVnjfHHAGyKl5o98nkeISCHoiSiN90vokNtZejxldjeJ/+\nMcNQKqEGgGg6hnnl8vb2J+VWtW/3vo8b5l4DQE5O2Tt4ALzIY/McrdNab1JRt2nBnPLBiwAlQISA\nRZULkBPy2DOwD0tCi3BF3VrdZ4/gnw//BMOZGGr81bhr4e0IuAL4+fFnMSqkjK3saR5HMu9hXnwT\nmiuaAMjqzL6hg8iH2iAkm+DAwfmCHCfgQFsUG5fXFk1SOHgqihwnYP1S6+505zvWLYnglZZuHO0c\nxfqlkdJvcDApPPfWKbx1oM9WcCvE6mZz63iCmpAfm1fVqZnUG1dcmOfaTMIh1LOMUh7qwtbjQHFv\npzWhluDzaBdXlqHtG7vopvn1ICQ6neNBgrE4XrT0T+s/J501q66ar3Yqlg9SgCiCphkIkMkfxQiq\nVYIXJTDhAeyLncDy4UZsrF9vWIfBQ1zENvPr9ldxcuw05rk/Y5tSoXmoNYX6V6d/g5GyToC9Ebwg\nvw+0csyknPLZZoWare9CqjIKAbWW5B2QZzLqwn717yyfxc+O70CKS2NdzWWWTVVIuguBvviQ50WA\nVm7QJFnRtbN8eFyM4WZGkiTkeREeF4PuiV682vUmhtNR/NGqL4CirC0frE6hBoDhdFQl1OT5DJ/F\n2z3v4UtzPofW0VP4+fHnQFM01teugYdxgxN5dVmKNivUHC8CjPx9CrrKcN+y2/GdD3+AN8+8YyDU\nb57ZjeFMDNc3bsHvLbwdbkY+hgFXACPUhMGTT5ePojVzACc/Oogb5l6Naxquwi9OvYhjI61AGBB6\n6uHAwfmC1/f14IV3O8DzIq65fI7tci3H5O/RVSsvzCl4QqgPtEUdQj1DVLDfaQAAIABJREFUOHw6\nhl17exAJ+bB2UTWWzqvE4sZKVYAjtTeSqNQGMRRqKs12Dz2ISi1K0iVv9wAcQj3rIAp1qVbggEZ4\nivl/XRZFh4JoTASRkwzsFWqGpkykjqij+qQPolAXoi85gFc634BUFgKXMlf0TrUo8fjISUQrWgAs\nUdpjAwKJ0KN1CrUgglII1Vhu3LCO9/pa0J4aBeAzFuUV7ENREtE90Suv2s2r21l485LIyCSUxOYJ\nooCehPw+ipYj9XhBVAl1TiHUJtIqSqBobVtESQJTsO95QUSOEwwK9bu9LUhxcovmrokeMEytuiyB\nIIjwujXCb4rNY4h6ngMgWRYl0sERZOuPAqMr5JkOmtGOH0sjriR2tI23Q5LkvGnOpihRXyQ4lJb9\nj6IkYiA9hIivSibUve9j6/It+NnxHZAgQZAEdMa7sSy8GIOpIYiSqO4rnjMr1OT4+1gvIv4qLA4t\nxInRNoxlxxHyVkKSJBwdaUWA9eOzi+4AQ2vnr4dxK+eTRqjJ+miKxls97+GtnvfUv0VJhCDZN/5x\n4OBs42jHCABg38lhW0IdT+VxrHMUC+qDqK+afKOW8wlNdUGEgh583B4zzcQ5mDpyeQH/+XobGJrC\now9ugr+gvsmD6dlqakJ+fP7GRUhlOFSXIN+XApyzdBYhSZKqUBcmFhDoiTYhY8UIqVXecWFxml36\nBkBaZZsVUp9Ft8QcZ1SoBVHAq51v4vv7/gmHo0fBBXvVCDLDNqlFiaVPL07k8UzrL5DwdIHyplVL\nigg9odZN0TPy86NZI6F+restHJpokdep3G1TlFmJH05HkRVkCwTFcup2FqIw5aM/NYS8qKjxtGz3\nEASNLOeUdVoVJVLKmClatCxyyxQ0dclwWbzZ8w7kYEKgK35Gl7BRcNxpe8sHGZsEmfibY/MkMFWD\nyPp7QfmS6r4nx5RlaIznJ+T9kU9iKB0Fy1CWCjXFCIhlRhH2hgAAwxm5Qn8sG0deyGNesBE3z7sO\nGT6L7W98H4l8EquqlgMATo21AwB69cWMtGD6zuR5EWDlfeVV/Nmrq1cAAI7ETgAAepJ9GM/FsaJq\nmYFMA4CbkY8lL3Hq9hMV//NLfg83zr0GIU8l/nDp57Cu5jIA8nmoL9R14GC2IEkSTp4Zwz//6ige\n/vHv0BtNGl7P5Hi098nfxxNdY0hZzA4CwN4TQxAlCZtW1s36mGcLFEVh7eJqpLK8mlbiYPr49e86\nMTKRxW0b52H+DMfa3XLFXPz+tc2lF7wE4CjUs4hsXgD5LbYinkABQZqEQu3W2RoALbNab1lg6WKW\nD8mU8AHoLB86L22eF+BWiEtfcgBPH38OPcl+lLkCSHIpUDQPXjB7kFXVsuBz4rkJ/PjwT3Bdw2Zs\nadgIAGjp36flFivKLwAIEi/f7dG89pygkUS9Qs2JPOK5CaX4TFI7Jbo8PP5+/z/hM82fwvKqJQCA\nzokebUAsB4AxRd0BMqFmGQpepSCma+KM9iIlkz1BFAFKUajFrPLZBZYPQbOFgBIti9zUroSKQr3r\n9DtIcWncNPdavNXzHronerA0vElen76DpK4osWVgP16LvgMwq7QcbkY3FoaztnwoCq1+bJxBoZ5Q\nlz813g6Gpk053ACQpycgiRJWhJfgw8GPVIV6ICX7p+sDdbi2cTPePPMOklwKl1evxP0rPo9H3v07\ntI13AND5p0GKEgsVakFVlAmhXlW1HP+NX+HIyHFc23gVjirEenX1ctN+JoRaogTFA6/ZYsrcZbi6\nYRM+t/hOAEAnOd6UCEkCLuEkKAezgJF4Fq+0dAGQC5ZZhsaRjhH0DGsk+t1D/fjDrUvUv1u7xyBK\nEoJ+FxJpDodOxbBltdmS9MGxQdAUhSsv8Cn4dUsieOtAH15p6cahUzEMjWUQi2eQ50RVNFm3pBoP\nfnrFuR7qeY0zQwm8vrcHkUov7tjcdK6Hc1FjVgn1E088gcOHD4OiKGzfvh2XXSarPkNDQ4ZOXT09\nPXj44YfBcRx++MMfYt68eQCAzZs346GHHprNIc4q9GqvXWSa0fIh/7hPKjZPkJcVJSWzukChztgk\nSgiiaFJtAU0dJeROkiRwnAiXC3i180282vVbCJKATfUbcGfzrfj27x6HxGj+cH0DFa0o0fg5r3e/\njb7kAJ5r+xUagvVoKJuDXd1vqa/LCq78XlWhpgR1W/WK4phOoR7LjmtJDrQAXvEQu4IJnEn04a3e\n91RCrSfGFMMB8Fq2H09m8ijzudQbBQOhJgq1zkMNAGB4c1GiwfJhQ6izmkKd5bN4qfUN+FgfPrXg\nJhwbaUV3ogdUNdm3BU1lGBrdEz3Y0fpLCJIA2tdkUqjlbeUtOiWKoFxEPdfNBJAZBobGRC6hLn9q\nrAMsM98y6jFDycdjTlk9Ir5qDKejkCRJ9UTXB2rgYdz4g8WfweHxo7h34efgY32YF2xE90QPckIe\nvcl+UKDgZlzI0iKsPdTyeU0SRKp8IcwJ1KFtrB05IY8jsROgKRorqpagEB7arewLmazzgqTOHpDX\nCFhauTTSourrd+BgpvDa3jPYfcgYL0lTFDYsq8ENaxvwzy8cwb6Tw9h202K1+PBo1ygA4O7rF+Lf\nf9OK/a3DJkI9MJJC50ACq5urUHGW0j1mC0vmVqLM58KJ7jGc6B4DIPce8LpZ+Dws8lwOHxwbwn1b\nlzpJIDYQRQk/33USoiTh/luWwjMDKSwO7DFrhHrv3r3o7u7Gc889h/b2dmzfvh3PPfccAKC2thZP\nP/00AIDnedx///248cYbsWvXLtx+++345je/OVvDOqtI6wi1nae5mOVjMrF5Vl0JWZoqkkNtbfkg\nTVL0MX8SgEToEF7uPIVKTwXuXfpZrKpeLivSoCBRvDqWQkIt2y20MY3n4ni//0OUuQJIcWn85Ogz\n2DJnI8Zzcbhpl2ynUFRSUZQgUUTV1fzUvKiprmNZrRvfaHZM2xCGVwkl4xchAirZ8jBudMc1Yiwx\nSrGeRXOXRJpDROcJ69Yp28RDLegIPiATdMskDT2htjgu5Dzxe1m0DOxHIp/CHQtugY/1oal8HgYH\nhxEXZO+kesOhFFPSrJzHrHp9GR55XlF3GV1yB8NbKtQUqx+bhUKtWD4CLj/axtvBME2GbRBUQi0f\ng/pADWr8EfSnBhHPT2AwNaw8L6tlG+rW4lOrr0U0KhP1xaFmdCd60BHvQm9iALX+CHJCHjkmZ7r5\nMHioGa/6/OrqFdjV/Rb2Dn6EM4leLAktgo81+/mIQk3sJPKxkdfnYQsJtXI+U6Lq63fgYCYgShIO\ntEUR8LL41hfWgRNE5DkRNSEfKss8AID1SyN49/AATvWOY+k82UZ1rHMUXjeDq1bW4c39vTjWNWpK\n5/ngAi9G1INlaDx8zxoMjKZQG/KjNuRTu+MCwH+/dRqv7T2DjoEJLJ8fOocjPX9xoC2Kjv4JXLm8\nBquKJHY4mBnMmoe6paUFN98s570uXLgQ8XgcyWTStNwLL7yAW2+9FYHAhVk88fq+Hjz27/ssCbP+\nYleM4KrLFFo+iijUZFmr9sksa91wBVCyi60UauVCRXx5RGnNu0bgoll8+8q/wiplGp2iKHhZLySa\nM4xXv62FY3+jezd4kcdnFt6GTzXdhNHsGF7qeA0u2oUtc2T7h6b8iqB0yq+geF71lo+skEWGl6Pq\nRrKj6rIUw4MTBHCCCFrx2/Iij9bRNuQFDn2pQbhpeVtF2ppQc7yIbF5QEz4yfFYlhmScvEBi83Tb\nzpptFXoVm7KxfGSyWpvvfiWHea3i4W2qmAsAGM4OKPuW2F9ke8tI5T6MZEdR5VWyWWhBSznRk33W\nTPY5XgJNbCGUqKWp6M6/eG4CPtaL5eElSOSToL1py6LEpEQIdR1q/LKcPpyOYSA1BJZiUO2zvpgv\nrpS9dx8M7EdWyKIxOMdUPEiQ16V8EIUa0OwdL7XvMvxdCI+OUHOCXFRKzid3oUJNyQyasplVcOBg\nuugaSGAskcOaRdVoiJShqa4cS+ZWqmQaAK5YJhPivUpr5+HxDIbHMlg+PwSWobFhaQS8IOFwu9ZN\nUJIktBwbhMfNYO2SiyMZY35dEJtW1GFBfbmBTAPA4kY5MvVUr+OxtsMBpTnOpzbOP8cjuTQwa4Q6\nFoshFNLuGsPhMKJRc+ej559/Hnfffbf69969e/Hggw/igQcewPHjx2dreDOGdw/3o3sogbFE1vSa\nXqGeTKdENeXDJqEC0Ei2tixp1GJWqK2KqXjR2kMd8MkEgrTAJiRZpDn4WT/8LqPi52U8kCjOMF4C\njpcMijlRp6u8IWysW49PLbgZSyoXAgCubbgKYa/SyYsSFeVQUr3JAMBDl+WtI4lEpR7N6BVqjVAy\nLm3ZI7ET6En0QZRELA0vAgAItJzMkSsgwUmlSyJp6nJmohcSJJ3CKau5vM5DDVjbKgRRAiitwNKK\nnOmP95jiJyf7pKlctj8NZuXpYZJDLUe+jSDt6cHiymbcOv8GdQy80tjFbPmw8lDrCiatPNT5CZS7\ny1XiKwVilkWJSWkUAZcfZa4Aav3yj/lQehgD6SHU+COmAkGChZULQIHCgeGPAQCNZXPkmDubHOpC\nDzUAzC+fi6CrDCleTkVZXWXtqSTxeRQjaOeZsv3qsVWgWj4oEaJDqB3MID46KZPkdUXi4JbNr0TQ\n78JHrcMQRBHHO2XRYOUC+caZ5Et/dFL7TT3WNYpYPIv1SyKXxNT+QpVQx0ssaUTL0UG8daDXVuS6\nWCCIIo60jyAU9GBe7aXdcOVs4axNZFqRu4MHD6K5uRllZfLBvvzyyxEOh3H99dfj4MGD+OY3v4mX\nXnqp6HpDIT9Ym9bYpRCJBKf1PoLxRA79sRQAwOPzmNbnOqPdOQuiZPl5bo92103RFCKRIHw++eIZ\nqvSb3lM9mlHfF4kEAVb+u8zvVpf1+2RyEAqXmUi5JAGMPwMmICDs11rSevyyOsIr4+SUhAmR5lDm\nrTSNo8zrRyIn2xCC5T7D6xLkQhvy3EsHfgNe5HH3qttRXyvfZP319V/Fb9vfx62Lr8N7XXuV7RdR\nXuFHKOQzeJMlike4qgwsy4ASdb50Tw6RSBDJdm3mg2J40AwDThDh1xHq42MnsahGJqcb51+OI7ET\noEhihM9tGH8iL392TVUAkUgQ70fladTlkYU4PHgCFC2gvMJnUoEZNw+JogzrEiVjUWJFpR+Rgm5S\n5HiHQ35MRCcQdAfQUCcruqGqxXAfcGGYGwBQA5eLRSQSRDyZA+WWbwhuXrwFXpeibtECaJaB280W\nFCXyoBjaODbI9hVJeR85jsMJWbkPBFmkEmksCM3FxoWXYcfJnRADMQjiHHU9vp44QAlIiXEsr1qM\nmppyLKXnAyeAzlQX8kIeTVWNpvNH+zuI5vA8tI/KLcdXNi7EyYk2ICFAgGQ6r8hNUn11GJEq7bX1\njauxu7MFDeV1WDG/CVYIj5Sr+yhY4YPLzarHr6EmjEqftr6KqJIJTouoDPlRZdF+txg+6bXFwcUJ\nSZLwUVsUHheDlU1h2+UYmsb6pTXYfbAPJ8+M41gBoZ5THcCc6gCOdIwgk+Oxv3UY//bKcVAAri2S\nT30xodzvRn2VH6f74kptUGl9cH/rMJ56WRbqdh/sx5c+tQzNc2Y29eJ8weneOFJZHlcur7VtzuZg\nZjFrhLqmpgaxmDYdNTw8jEjEeEe+e/duXHXVVerfCxcuxMKFsnK5du1ajI6OQhAEMIw9YR4bS09r\nfJFIUPVxThf7WjUbQP9gHBVe4zgHdbFHHC9aft6ETtlOpfOIRhMYVbYpm8mb3pNOysuPxzOIRhMY\nVpbleUFdVlRUzMGhOLxu4yHmeBETte/ikdfex99c+Zeo8JQr75FveMbiWUSjCQwMJQBIEKQ83HCb\nxsFKLqVhiISh4QR0fWWQy/OgaaBnIIZft/8G7/a1oMobwoqylYb1XB3ZgtQ4j2xa8/HGRpIQOd6g\n/IIRMDQURyqdB9waSewa7sdc13z0j+vsGAyPRCqHPCeolpQ5gTr0pwbxWttuAECjaz4oUMgoimZs\nJGUYV8cZ+cfLRQHRaALHBk4DAOb55+MwTgCUPE5eMFo+WLeAdIYzrCsv5LWx0fI54Ibx5nJsXB5H\nOpVDND2KhmCtYR2NZQ3ojHcD9EokUzlEo/KUMVG+sykelIuorzwSyRy8LF2gUHNIKO9Vx5bntRsC\nZZvKXDSiI/J5m1ISPnyUH2zGhwp3EAkhCk53ro2NpUH55JvKane1vH2cbN862H8UABBmqgyfW/jd\nW1DWpBLqMqESlCB/j/K88fzPZDnQ5fJNUDYpIipqry0pW4LdaMHK0HLb7zWXVaw3NI/haBKJZFYt\nSkyM58EltfflMmS/SBgeTkDM86b12WEq1xaHeF9a6IumMDyWwRXLakq26b5ymUyoPzg+hOPdY6iu\n8BoabWxYGsGLv+vC9/5jHw6cHIbHxeB/fHY1lsytLLLWiwuLGyvw7uEB9A6nML+u+HepZziJf3vl\nODwuBmsXV+OD40N4/Of7ceP6Rnz+hoWGOqCLAYdOy/xrzeLqczySSwezZvnYsmULdu2SPY3Hjh1D\nTU2NqkQTHDlyBMuWLVP/fuqpp/Dyyy8DANra2hAOh4uS6XON1jOa1UBv7yDI5IweaksLhqXlw1xo\nSKB2AxTsl9Uyi82fJ4giRDaLBJfEz47tUBtp0DQFn4dVPdR5Xs4ulijRssDLy3oASlZfTZYPQQTl\nj+OJvf+Ad/taUBeoxVdWP6BNoxdAXwAmCHJRop6oUjRJZRCtLR+6okSK4ZEjcYUKWSJd9KKZEZS5\nAqj2heF3+cApLbkLY/Pa++X1zq8rhyRJ6Jo4gwp3OSLEB6zYEeTYNW2cjMts+RB1jUHs/LhqBCKT\nR17Io8pvLLBpKp8LCRLogNblT9A1lWEZFzyMolArlpfCfQWWB2eyfEiQlKI8fVEi8VDztDz7UeEp\nB0VRWBxaCJHJQnQn1XOZF0TQPpmA1wXkaeiAy4+Ay6/mdtcHirekJXaSCncQ5e6gas0QJON3Ks+L\noJUiSr2HGgAuq16Br6x+ALc13WT7OapPWrF86ItKTZYPSrN8CE4OtYMZwn7F7jGZ7n9L5laiIuDG\nniODyOR4rFoQNiiNGxTbx4GTw6iu8OLb96+/aLzTk8XiRvnmoZSPOpnh8KNffow8J+KP71iBr3xm\nJb75h2tRG/bjtx/14vm328/GcM8qDp0egcfFYNm8S+cG61xj1hTqdevWYeXKldi2bRsoisKjjz6K\nnTt3IhgMYuvWrQCAaDSKqiqtWOnOO+/EI488gmeffRY8z+Pxxx+freHNCE7qLB2F1daARrI9bga5\nvGDZ4tqy9bhC8Cw91CTlgysoStQVGpLPsPKICYIIRlF/28bb8Wrnm/h08y0A5KQP1UPNCaaIMj1U\nDyvNmzKvBUGCGDmKdHYMt/z/7L15eBzVnTV8bm29qLu1y7ItyftubGOMARtwIDAEzxASEoiZCWsm\ny2Qyw/vCzOSDfJMEngQm3yQThuRhAkMgCQmEkIQQCPu+GW94t/FuS7ItWbtavdX6/XHr3qrqrm61\nVsu2zvPwYLW6qm8tqj733PM7vymXYPW0yyHnIdMAINtFgtRjbCdhuBVq1pnQjqCTBRmaqaEz3c0z\nqBmIoDuTG1uhPrt6Ef5y8BXoloGpsXoQQlAihRHPUMKY7S3e39wDAmDm5Bi6Mt3oVeNYXL2QHwMj\nxtkpH4Kcm6RhQHNmrXmKEnn0nEWJaVXYuxTMfNRCSQ/fXndNOmTBIdRE0HlGqzvlQ5QMqJmsokTD\ngCA4Hmp2v/D7kFDlnK1iTItNwcbWLRBKevm9rBsmiE2oJ5U4yQITwtU42ENV54klhRMHZpRNQ1AM\nYHrZNAAOuWXFqHy8usltOu6UD4AWyi6uXlDwcxRPUaLJOyVKRIJAvH9rbJKXrxnPOMYxGHy0tw2S\nKOCsIhIXBIFg2ZwavP4R7dDK7B4Mk6tLcPasKoiSiC9ePgux8KkdkzcYzHL5qC9bVs9ff+K1vTja\nnkRlLICJlWFsP9CB9p40Pr1yKp/MzGkox3dvORd3/2IDXt/UjGVza04bdf94RwKtnUksnV192inv\nYxkj6qF2Z00D8KjRAHL80bW1tTxOb6yjN6HiWHsCokBgmJZHjWZgr8XCMtpUw7eFqltFdmLz6Guy\nT7ydkpVDrdtpD8fEbdjZYWFB5RxHoc4id5Zl8Xi1KdF69Gl9ePHw65hZNh1zKmaiJCijpZOSqEye\nAjCGECNwou6rUMtSBhG5BFfPuDJn22xw8kIoac7Jd7YTHwy7iK42MgHNvcfRnenmGdQxJYpeNQ5R\nNpCMM0JN/18aiPEW1YychuUw2lOdyG7JrRsmDh7rxeTqCMJBGR+foHF5U2P1kEVG/E27MYi3eFKQ\ndE/iiWVZMKCDu+TzkDN2vdO2hSFXobYJdaTbUZFdCrQsSAhmKdRaVlGiIOtQE1kKtamBfwW7CiY5\nsSa2Qq3Yfmk+iaKfIYkCdMPiCvXEEqczW02IEupCCR8MISmIu5bfjrBM989SWFgxKgObJIhEzLva\nUQiMUBN7hUE3LEAxnPQPF9xFiadjysdw9Qj4+OOP8d3vfhcAMGfOHNx9992jfiynClo7k2huS2DJ\nzCreSKs/nDuPEmpCkBMNRwjBP31u0bDYF09VVJeFUFqiYF9zN28wtq+5G69tbM5579mzqvDpC6d5\nXlNkEbesnof7Ht+Ex178GHffcm6/VpxTAVv30/qmJTPH7R6jifF01UFij90Ode6Ucuw81Olr+WCq\ndSysoK077W/BYK2b4SjTTqfE3D/s3BxqE0RJ4xDW48Gt63Fe7TmAOIf+LosIuIlqLBDFtbOvxg83\n/RSvNr6FORUzEQ5KyGiUvKqawRXOsI/lIyAxAqfnxubpJiSiIyAWV8jlUagNSqq96Rl0TJpNIkuU\nEGJKBJ3pbm73mByZiN7OOATZ4NfCFDQIRIAsSDiv9hx83LkP8yvpuSmRwzBB95dxWT6OtMah6iZX\nPpriRwHQCQhPqiDM8uETm+ci556CRLD86txVA3b+kkyhLvF+cVYEyxAUA0gGE9ATbGXCOUeyIPEc\nZUEyoKVpcxt3UaKQZfkwLQsGcd2z7k6J9ngyFvVGlwboueATCtd76f2XgkwURGQn+pJF5xVK+HCj\nMuQcMyO+JnRPF05NNyGLGkJScFBFNgEfhRqCAUXMnTBmN3Y5nTCcPQK+//3vc0J+xx134O2338aq\nVatG/ZhOBWzaSxM5lg7AljGzrhR11RFUlQZzYuPGQScVs+pKsXFPG9p60qgpC+GZdw4CAO79h5Uw\nNR3HOxLoSahYsbAWgs9zY+bkUly2rB6vbmzCs+8dwrWXzBztwxh2bNnfDgJg0Yzx7OnRxDihHiSY\nf3rprKq8hJor1HbHKj8LBiO9wYDoIcmAv+VD8iHUjPgSEKxr2QRZ2Q2iLMu1YrgItSxImFbagLrI\nJOzrOoC0nkZJiGVR04g1ItEldz+FOmiTEJJFqFnDEUvQqc+6CHjVQNPOm86yfBgWdFMHIZTMlwfL\n0RQ/ivYUnYlPKqnF7s69ECQDCdbtkTjk69zas7Gwah5XWcMSTXEgkuaxfOxrov5pRqhZp79JkVrH\nq80tH1lZz3bzFEYCDcPyqMQgZs4kB3Cud8KwFeqQd2mXEIKAGEDSbvXOtvGzfAh5FGoi6Z6JAz3H\nzj3rtjawz8iArlbEbIXabXlxjwO2DcdNcll0Xn92Dz9wJVl0bFKWZUHVDUiCjqB97Qa+3+zYPBNE\nPPMU6nw9ArJrXPrrEaCqKo4ePcrV7UsuuQRr164dJ9Qu9KU0HGmJ43BLL97afAwCIQMqEhMIwXdu\nWTae0lAAs+rKsHFPG/Y1daOjO4WPG7uxcHoFzppJi6QnVfXf4+Kai6djy/42vLS+Ecvm1mDaxFM3\n+aMvpWF/cw+mT45x7jGO0cE4oR4k9jR2IyCLmGdHH6XyeKhFu9gPyLVguF8LByT0ZWVAZ/utgdwc\nanc28yfqV8KyLLzV/D7EilaeWcxgGBYIYTYBSi4WVs1Dc98xfNy1n3dLTKRoExDi00SDgZNl0duA\ngyruFiyiOTaEfiC71UBb+fUo1LblQ7OL3AKSgvJgGQ73NuKw3cFwcoS24CUu37BBNIRd6qP7OEpY\nrnaWqrz/KCPU1Et3PNHK85Xjap89HhM6s3y44/3szousFTu9Nu6JgX+mMbvefTr1gleVlMMWh51z\nJMogQoYT8mzLhyLIICCeHG6mUEuCBEvUPAp1dqY3fDzUaZMp1DH7c+Sc97JzIBHvtZ5eNhWVwfJ+\nfc1+UFwrFswmZZgWLMuZJA0GvL04K3I16TkI+Ez8JHL6eqjb29uxYIFzXViPgGxC/fTTT+PRRx/l\nP7MeAbqu45vf/CYqKysRiznEo7Ky0rfXQDZOZtTpcKK/sazdfhz3/XI93DWtFy+ZjGkN+ePyRmos\no4nRHsvysybhydf3oak9iQ/sTpG3fnrhgMfyf65fim/9zwf4n2d34p+vW4Kz5xQuph4oRuu87NjU\nBNOysHLx5LyfeSbfL4Uw1LGME+pBgPmnF06r4M0/8inUoYDEPc3ZXmPAUQNDAQlddv6vY/nIVagV\n2atQu7sHhsQgppdOxVvN71PSo2dbPkyPQg3QrnIvHX4d29t3oSR4Dj2WtE47JRYi1G4Pte5D1Ah8\niYofpOxiP18PtQUDGv/sSICSowPdhwAAkxihllwqLFERlPyVhhLZpVCzlA3Lwr7mbpRHA6gsDUI1\nNHSkOjGjbCoIIbnWFMMEsYm/SERYdufFjEYJNT3fXoXaj5yx+yKu94KAoDxYis6ENw5SEWRP63Ld\ndY4kWx0OiApU0XCKEhUDBAQxJYpuNeMpmNRsddYZm+Oh5kWSZgIhKcgVXEe1dZquaDpVyrM9zTEl\nintW3Ol77vuDu3hQN9w2FBOWoOcUJA5uvyZ0QwcRTIdou3A6K9SpGBQNAAAgAElEQVTZGGyPgEce\neaTf/fjhZEadDheKGcurHx6GZQFXLK/HzMmlmFIbRWUsOOzHcKqdl+FGRCEIyCLe+qgJqmbi7FlV\nKLPFoYGMZWJpEJ//xAz88e2D+PbDa7FyYS2+8MlZEAjBoZZeHDzWi7qqkkElqYzmeXnXLmKdNdH/\nM8/0+yUfih1LIdI9TqgHgV1HaE7xnIYyhAJUafErSkymdYSDEleVfQvSTFpUGAiItl3CUf/8YvNE\nQYBACCc9TGUDKGFwCudyWzd7LR/0fQ3ROsSUKHa2f4yLA8sBAH1pDWq/CrX9Whah1lwWlGIVan/L\nh1s9NaCbpp17TRXqUoF6bk+k2iEQgUe2OQo1JeDBPOQrzAm1yhXq1q4U4kkNy+fRfbUmT8CCxYvt\nZNHtq2XEn24bU6KIGzSGj092TK/lg+Tx47Lr1Kv1oCxQ6us5lgWZxwcC8CShMCtDQAxAta8H65So\niDJCUhBdQh90gyrkgkBsj7XrnnWNjXu6jT6UBmOuMTjH78T3WdzyMVzg1gzB4BMId2qJnwWpGARc\nVhLdMKEauv15BQj1aeihHq4eAeXl5ejudpKOWltbUVMzvKreqQrTtLDrcCcqYwFcd8nMccvGCEIU\nBMyYHMOuw9SS95mLpg96X6vPn4IFUyvw2Iu78f6OFmzc00Z7Gti/VyQB9//zhTn9HcYKTMu574qx\nuoxjeFFUDnWxysOZAE038ef3DoMQWkErCgICiugbm8cUatG2bvgq1LoJZeYW9FZ+CIBGuBVSqNnr\n3EOtO0qjIioun6sPoXZ5ehlhEIiAhZVzEdf6kJapHzmZtn3FPDbPJ4c6T8qHezz5yGw2eOavx/Lh\n3HPU82pBt3OJA1IA5XahHACUBUq57QE8V9k/r5ihhPlwRcfywbJM3XYPwMlXZqSREHeOsVPkaRDa\n6IbtT3dlRfPjy2v5sBBX4ygPlub8HrDJvGB60l2yr2VAUmAJ1MfN2nQrooKQFLTbxFte730W2de5\nh5pabtJmits9PMfvfq/dfn0wqRv5oLisGex89TfBK2q/Wcq3AXuC5kOo5VOk9fhgns3D1SNAURRM\nnz4dGzduBAC88soruOiiiwZ7KKcVjrTGkUjrWJCVHT2OkQF7Zp87twb1NUNrsz2lNop/v2kZrv3E\nDETDMuY0lOGvL5iC8+dPgKqb2Lyvvf+dnCQcb08gkdYxu758/L47CSjqW/CSSy7B1Vdfjc9//vOo\nr6/vf4PTGC+tO4KWziQuO6cOk+0W0uGAlGP50HQTqm4iHJA4MfZXqE0I0S6kRadhCyOoRRFqFzFS\nRMUhDcQhPQyG6aia7lzohVXz8MHxDWgzDwMop0WJuuFk/vajUOsey4dbSSzSQy16l9ezPdQ8No9Z\nPmwPNUNlsJyPKckIdT9j4JYPWUPGHv++5jwFiXZhncdDbFpcgRYgoEQOwyL0Wqguhdpr+TD87wHd\nBOQMTJgoD/jnoLLPNsxcss6uZUAMAKQLuu4uuAvR60foOVF1AwFFhOZTMOlO+SAybWvOIvO8x+9M\n1jRdB1GsEVGoc3zdQ1SoJUECgeBYPuyc60JFiWPdQz2YZ/Nw9gi466678O1vfxumaWLx4sVYsWLF\n8B/kKYgdvFX4eMrCaOCiRRPR2pnENasGr067IQoCrjx/Cq48fwp/7XhHAh/uasW6Xa24YEGt5/3J\ntA7dNE96Fvhe+ztszngzl5OCogj1008/jZdffhl33XUXJEnCNddcgyuuuAKKcmZVkLZ2JfHcB0dQ\nGlHw2YudP9xwUEJ3PON5L7OAhIMSRKGAQm2rnKZNIDWXQu1n+QAYobaJldvyIci+pIfBz/IBAHMr\nZkMSJBxVDwI4B4kUU6iL9FC7m9MYFk+PCBRr+SB+DVN8PNSWs183oa7ghDqAhNDHx0Vfy6NQ24Ra\nUnSoKYP6p5u6EVRE1NkTJUaomeWDd3R0d0okJiQiOdGCNmkFkJvyYRczZkMzTAgBmvnsPi43WKEe\na3bCzhEBgWgX0AXFACw7mi+jGbTgTlT4CoPb784yvZ2xOfYKFsUIwKtQuyY+nOiazDYxjAq1x5rh\nIvkFJnjFQiYyDHu/bMXD1/JBXN07x7BCPdhn83D1CJg5cyaeeOKJQY7+9MXOQ50gyM2OHsfIoCIW\nxFc+PfAC6IFgYmUJptRGseNgJ3qTKifPumHie7/aiNbOJOY0lGH5vAk4Z041oieBXO9rYqus/iud\npzuSWgq6pfNkqtFGUZaP6upqfPGLX8Tjjz+O7373u3jyySdx0UUX4cc//jEymUz/OzgNYFkWfv3K\nXuiGib+9bLYnmD9kK9Tu5VdGqEMuhdo3Ns/2uppEB0CjwTTDBCHgRNy0TBzra+H7z1aoGWkLiIpv\nEoPzWa5CNhcBCogKZpfPQIfaBqKkkGQKdSHLh6tTottD7SZqRSvU3K9qWynMrMg3kZJEkzDLB808\nZioiV6jFAExid9djk4F8Hmrb8iHIOjp60vjOoxvQ2pXCnPoyCPZ5dyd8ANQeI0LkXmhGakXBIdTu\nGD49q+MjyUPOdN0hsPkINfPGMxJICyINCBD50p47ZzmZ0WnGsqA4BFTUKdEGcmP1XDYOzVbMAS+h\ndqu2LG2EtRcfVoVa8BYP8jEN0JvvB0mQXSse1PLRn4fabxI0VjD+bB57SGV0HDjag6kTY7xofRyn\nB86fPwGmZWHTxyf4a+9sPYaWziRKIwo+buzGr17eg9t/+j627h9da4hlWdjT1I1YWEZtxeCiRU91\n/M+2R3Hvuh9DNbT+3zwCKIpQA8CGDRtw55134stf/jKWLl2KJ554ArFYDLfddttIjm/M4HBLHDsP\ndWLBtAosm+Mt4AkHJFgWkFYdgsIsIGFXyocfodYMw/EL2x3odN2ELAqcKL3R9C6+v/6/8OC2R9GV\n7s61fIhuy4fjoc5WxN1ENZsALaycR4cQ60Ai7SjUBMR3SZyRZWLHtDnH4+qwWCTxcRqm5Ld8GIYF\nE46NQyAC91FX2LnNQTEIixgA3F0eC1s+BElDIq3jaHsfzp8/ATdcQRu/sISPiSUTPF40UZC4F5qt\nDrgVaiJqfPXAMK0sFThPbJ5hQQxS8pPP8sEIHifU9mqDSNwTI6fZTiqjAsSiCjXPDNc83ntPUSJx\niu90oz/Lh5M2ovHCvuG0fLg6GnIPtTlkDzXgLu40YSD/Sop0iniogfFn81jDnsZuGKaV0yp8HKc+\nls+bAAJg3S66eplRDfz5/cMIyCK+e8ty/PDrK+wiVOCXL33sW1s1UujoSaMrnsGsurIz0j/dkmjF\nwZ4jiGt92Hxi20kZQ1HrtJdffjkmT56M6667Dvfccw9kmX55zpgxA6+99tqIDnCsoLOXEoyzplfm\n3KxhO6KHFSEC+Qi1T6dEyzWTspfkaetuZ66zp3M/AGBXxx58b91/QYkuhtZV6eyTe6jlLNKT5aHO\n6q7nBmvEQZQ0EmkdpmWBhHUEREpesxEU/Tsl6oNQEgUiQIAIk3mTPZYPQicHuqNQM8JVHihDW6rD\n46GmYzL69dsGpQCNqCsXcMGyenzynMmoKXdm9dkJHwwSkTyWDxoZJyPEc611rlBnp3zkaz2u6SbE\nCFWoK/qzfMBRqGn+cy6hJqIB02RkUfGOzSe/nI/N7aFWmELtLB1yG4QrbUQ3R4JQ+3m1C1uQioVM\nFEDstYsSdYg4tT3U48/msYedtn964TihPu1QHg1gTkMZPm7sRkdPGh/uakFvQsVVK6byJiqfOq8B\nqm7gT+8ewh/eOYAb/mrOqIxtLyuqrz8z/dMbWrfwf39wfD3Om3jOqI+hKEL9yCOPwLIsTJ06FQCw\na9cuzJ8/HwDOGP9cX4ouD0d9lvA4iU7rqLBXyFmjl1BQ4g1a/DslOoSaddrTdZOTcMuycKS3CZXB\nClwx9RL8Yd9z6K3cAO3wJ2FZlidiThEUiESkDT58FWpvp0Q3mOdIUFQk0hpEQQBEDWHZv324JEiQ\niART1D3Hpbs91EVaPgBKVHVi2pYPh/grREFaMJDRDCc9xG6zPTFSiwM9h/lkwFHNdUfNzGP5EIiA\nsByCJOu4/uJZOb/PTvhwjxMk43i9szzURNRcHuqslI98lg/DBAmkYKGA5cMm1KatUDN/tuTKUGbt\nxyHoIPb9o3gUap0nkOTkUAuubGmXQu32oomCSIv6XB5qVtinSMNp+XATakbyHQtS0MeCVPS+Rdle\nCTJg2oTabzJwquRQjz+bRx9b9rVjUlxFTdTfI7vjcCcCiojpk07dbnvjyI/z5k/Ax43deHPzUby5\n+SgiIRlXLG/wvGf1+VOwfvcJvPXRUVwwv3ZUmpfstbv8zq4/8/zTlmVhY8tmKKKCushE7O8+hNZk\nG+cGo4WiLB9//OMf8dBDD/GfH374Yfzwhz8EgDNmaaEvRb/MI+HcL99wlirt/rdHofbrlGh5SU22\nQt2WakdCT2JaaQNWTjqPWjOICUvUqO3AsDyWD0IIRCL5Kmtu8i1lWT4YcZICKvVQazSmrJAaGBAD\neXKoWWxe8YRaFERXvjNVfgWIkIgCiAa10zCvuE2cr5p+Bf6fc2/jPl+3al5M0kiJFEZC928ykZ3w\nwSDZlgGDd0o0ILk81HA1itGzUj4K5lAraciC7MT5ZYF5qA3QAkqmULstH0GXQs3Plai4lHvX2HST\nRwxKRAYhjldY0/2LEgHweyunKHEYPdTuvGi/HOrQACZq2VAEBYQAqq7BKlA866jxY5tQjz+bRxcd\nPWn89I/bcc/PP0RPX65Hvb07hdbOJOY1lOctKh/HqY1z5tRAFAhe+PAIUhkdq8+fwlepGSRRwE2f\nmgML1Pqh6SYaW+N47IXd+NcHP8DBY70D/tz+IjL3NXcjoIhDjg08FXGotxHt6U4srlqAVXUrAQAf\nHFs/6uMo6i9+3bp1uO+++/jP999/PzZt2jRigxqLiCdtQu2jUPsS6nRxHmoWBQewFAZKqhmhZq21\np8RoJFYgq0NhdlEi4NgSfBXqPJaPsByi1gtFdWLzRL1glnRQCuR0SjTcHuoBLM3TMXs91CIROYFN\nq7qHJAJ06X9SxLFksM9zK9SFxl8ih5HUUr4PquyEDwbZ9lDT1tUGiEAj45iSTySnxbfbYgPAowK7\noekmIKVQEczvffN0aTQtXvDovo6cGAq6Z5IV9kn50E1HoQ6JIY8dhXmo3V0SGUSIXsuHrZhn309D\ngezTKdHtoS4239wPzC6U1NI5fzduUDWejHmFevzZPLp4/aNmmJaFZFrH028dyPn9zsMsLm/c7nG6\nIhKScdZ0arksjwZw6dLJvu+bVVeGS86ejKPtCXz9/3sd331sA97ddhwdvdQqMhBs2tOGf/ivt3H/\n01ux7UA7zKzvrN6kiuMdScycXEpXl88wbGjZDAA4t/ZsLK5eiBIpjHXHN3FL4mihqDOvaRpUVeU/\nJxIJ6ProDvRkgynUvpYPl4eawZ3yUdhD7TqPIs0w1g3H8sEI9dQYXVLiiisrYMzKoQZs8uWKQWPQ\nC1g+BCJQlVrOIJHSkDEyAAHCcmFCjZzYPEdJLDY2D7AJjN2am6dnEBEyocfitXz473egCnVYDsOw\nDHqsWchO+GCQWQtw0+IFebIgeaLp0horSnQ13REC1CrhV5RoarAkNW9BIt3eZUEwLH7d3SsNbg81\n8VOoJcfyoeuOeh6Wwr4e6piSu2RNLS9OUSK7f7NXPIYCqg6TEfFQM/Kc0lS+P8Wn9TgACETMu6ow\nVjD+bB49pFUdb285hlhYxvTJpfhgRwv2NnV73jPunz4zcPHiSQCAay6eDkXO7WzL8LlVM1AeDaCl\nI4kF0yrwj589C5IoYG9jd95tstF8og+PPL8Lmm5i24EO3P/0Ntz50Fq8u/UYf88+Zvc4A+PyDNPA\nRye2IiKXYG75LMiChOUTlyKu9WFH++5RHUtRstKaNWuwevVqLFy4EKZpYvv27fjGN74x0mMbU2CE\nuqSQQp32sXwEJR5Vlq1QWxbNVmYXgSmIXoW6ESIRUR+hf8BuhVrVDU9xGSPJVNVN5yrU7vf6+EZj\nSgQ96RYYpok+NQMBQFDM71cNiUE75cOxNei61W/Chh8ocTYdKwWhdgY2OfAo1JICqLn7CPh5qAuQ\nLxadl9BSHjWdJXzMKJuaoxjLggRCLOiGzme/crblgxFqwwKI0zUyI6T9LR9CAiLy+6cB53oRuw27\nauggBFkKtU0MRR0w6EOeKtROyodTlEgVXwECAmKA2jhU5q82QCQtZzIB0JQTIqg5RYnyMOZQE0Ig\nQYZpHyswfDnU7Byl9UzORDQbIhGhkbFdlDj+bB49vL+9BamMjr+6cBouXFqHf33gXfz6lT34zi3n\nQiAEb285hi37O1BVGkRN+eB9/uMY+1gyqwr3//OF/TZyCQclfPumZYjEQhDtZ9n0STHsa+pGMq0h\nHCwsRPSlNDzwh23IaAb+8bMLUVUawusfNWPdrlY89uLHSKR1fOq8Bt7ld/ZpUpD41J5ncCzRgi8t\n/GK/mdK7O/eiT0tgVd0Knhi2YuJyvNn0Ht4/th5Las4ajSEDKJJQX3vttVi5ciW2b98OQgjuvPPO\nnFa1pzv6UhokkSCo5M5G/SwfboU6bpNx30YrnsIwqiDqhgVJFKCZOo7Gj2FyZCInVJykiobH8iET\nmadxUFtCrr2AeZPpe/wIdRQWOUqbk5gZBFGYvDASqpoOu3Ur5gP2UDOF2h6nSEQoggxCqKJIXB7q\nDHJJDi9AFPpP+QCAiB2dl9STqITTgCFfwgfgnDfN1J2CPNFl+RA1pDOs6Y5pN18RIAsKICR9I9gM\nKUkJdSC/upCd3qIZOiB5r2PAtXrBlPGAGHCK+EQdmqsokUb+yZyUMz+0Zmd48iJHF5idiN3LJvxj\nGIcKiUhQ3YWSujGolY9ssILWtK4WtHwA1C8+1j3U48/m0YFpWXhtYxMkkeCSsydjxpQKXLRoIt7d\ndhzPvX8YzW0JfLS3DSVBCTd+as64f/0MQLFdEUsjAVRXlqCtLQ4AmFNfhr1N3djb3IMlM6vybmeY\nJv7nTzvQ3pPGVSum4pw5tED+1tXz8DcXTMEPntiM3725H5JIsKepG5JIMG3iqV8I253pwbtHP4QF\nCw9sfhi3nf1VRJX8z7QNrbbdY8LZ/LVJkVpMi03B7s696Ex38eZvI42izTbJZBIVFRUoLy/HwYMH\ncd111/W7zb333osvfOELWLNmDbZtc3IBW1tbccMNN/D/PvGJT+C5556Dpmm44447cP311+OLX/wi\nmpqaBndUI4C+pIZISPZ9UPpZPriHOihB4p0S88fYAXSpnu1DlgQ0x49Btwxu9wDcBVuOhxqi4VGc\nmS3BV6G2P0/y8byymSCRMzxRIVyQUFNio5qOZYJZPgjIgEiWexLgTs+QBbZEn3HF8fk/yJzJRnEe\nakaCE5q3MHFvF/VGNkTrcsfJGqyYOiegsijbJI8Ako60lpXEQSTI3Crh0w7ePteRAg+N7A6YGm+o\n4leUqHvU15DLW+5WqCEYkAWnGRCzsOgWnSDlzWd2FSWy2Mfh9FADtFDSTWaph1qDIgSGRFbYpCNj\nZPhkNh+hlog45j3UwOCezeMYGLYd6EBrVwrnz6/l8Wif+8QMlAQl/Pn9w/hobxvmNpTh7luXY+F4\nu/FxFABrC55tF2rrTuE/n9yM+369Cf/55GZ871ebsPtIF5bMrMLVF03zvLemPIx/vf5slJYoeOK1\nfWhsiWPqxFhB+8mpgnXHN8GChcmRiTieaMUDmx9GXO3zfW9bsgPb2naiKlTp4UkAsGLScliw8O7R\nD0dj2ACKVKi/973v4f3330d7ezsaGhrQ1NSEW2+9teA269evx5EjR/DUU0/hwIEDuOuuu/DUU08B\nACZMmMDb2Oq6jhtuuAGXXnopnn/+ecRiMfzoRz/Ce++9hx/96Ee4//77h3iIw4N4SkNlLE/XvQKW\nj5AiQcrTKTG7Wx1EnW8niwIO9zYCAKbaBYmAt/BMM2yCphjUo2tDFmQQ4nh8GZhiSt9TiFCrRSm8\njMBpLoXaMKjlQybKgIiPJEgghBJV3rREkPhyfFpTgaABWIzU5vqeg654OIg6BCIUJHphmVk+vIR6\ne/suAMCCyrk52zhqruZqXU1XB0JiEAlR4w1+WAqISCRORA3dS840PdcD7weFWz6oD5t1KHRHvrk7\nJboLOGVBgkhEmJIT6acbFohoQBZC3K7BjoeR9YCPt1iyUz54C/ORUqgFGURM5nioh9IlEXAmXRlD\nBQn0Z/mQQEhmTHuoB/NsHsfA8eoGKu5cfq7zLI6FFVx/2Sz8+pW9+OsLpuDK86bwLqvjGEc+zJhU\nClEg2JPlo35lQxN2H+nyvDZtYhRfvmo+BJ/v0toKSqp/8MRHiCc1zK479e0elmXhw5aNkAUJ/+fs\nr+H5Qy/j7eYP8MDmhz250m3JdnzcuQ/taVqzcF7t0hy+sWzCEvz54It4p/kDXN6win/fjySKItTb\nt2/Hiy++iBtuuAGPP/44duzYgVdffbXgNmvXrsVll10GgDYZ6OnpQV9fX85y5DPPPIMrrrgCJSUl\nWLt2LT7zmc8AAFasWIG77rprMMc07NANE6mMjqhPZB7gEOrsosSgIkIQCFeocywf2TnFgoGETcol\nyZ9Qu6PRNM2xfLhJASNZqu5tv+luNOJHgKIBR6Fm3RvDBTJ/GdnWXYZmplC7CX4xcFspDNO0FWqR\nH0vGoEv0IvFfJaDj8SrUQTFYkNRHJMfywZDUUjjQcxhTovUoDeR6t1jesmHpDqG2xx6WQ0hKCWQS\n3hxqiQTsCYOVU3XsbgdfKHrOk/JhmNB5QaR/UaJbfSWEQBEC0ESn6YxuE3nFpVCzsenwNtDxGwdT\n503LAMHwK9SsGNXdDp2IOgLi0JY0Q7blQzM016TD/16VWJTjGPZQD+bZPI6BoelEH3Yf6cK8KeU5\nkWQrFk7EBQtqxy0e4ygaAUXE1IlRHDoWR1rVEVQkaLqJD3e2IBaW8cN/XEnFJcOCIgkF761JVSX4\n1+vPxrPvHsKFiyaO4lGMDA71NuJEsh3LJixBWA7h2llXw7IsvHN0LZ7Z/xfPe0NSEIurFmBuxWys\nnLQ8Z1+KKOOT9RfjTwdewNvNH+DKaZeN+PiL+hZUFPtLSNNgWRYWLlyIH/zgBwW3aW9vx4IFC/jP\nFRUVaGtryyHUTz/9NB599FG+TUUFrY4WBHojqarKP/9kIZHKH5kHOJ0Ss2PzwkEJJ5Jt+K/dP4ZQ\nvhC64Y3Xye5WR0SdN4ShCnUTQlII1WHHZ+UmjZrhpHy4SQEjZpqVRaiLtHxAzgAWsT+vkEJtE2rX\n5+g6TbYopLb6gREy3TTsSYIFSZC46poxVEA0IBa4ZT2WB1FHKE+mM4OjUKf4a7s698C0TJxVNc93\nG3ZudUvnCReMZIelECD28JQP3XRZPpiynbVqoOmuxjwFug26PdS6YXEV2atQu1JOXM1+AHqtkmKC\nF8hquk6vk6DwfXBCbWmQ4G+FyPZbmzAgwv9+Ggpoi3CTJ1aougEE9byNeooF84VrlsYnHfkmAyzR\nZCy3Hh/Ms3kcA8M7dprCZctyLWDAeN73OAaO2fVlOHC0FweO9mLBtAps2d9OCwyXN/CEr2JjzOuq\nI/jHa0av8G4k8eHxDQCA8ycuA0D/tq6b/RmcM2EJUrrzPR1VIqiPTOZFiPlw0eTz8eqRt/Bm03u4\npP6iAQUlDAZFfQtOmzYNv/nNb7Bs2TLccsstmDZtGuLx+IA+yC/rd/PmzZg+fXreIpr+gswBoLw8\nDEkanG+o2O5FSXuZvroinHcbWRKgGSb/fVozUFUaxAmjBbqlQyjphSyLnu01QniRIABaaGh/eQdL\nTLSnOrC4dh4m1DjFar2iHcckGAiFA4BIQEQTkWCQ7ztWEga6ACKans8LhRWuhk6aUEEVOBemgDYx\nIXIGMOitUVtZkfeYq7qoWmgQjb9HCUiAqiOshAbUHSoSDgFdgCBZkBQCaEAoEEBZJAK0UwJEBAOy\nQAmV376liH0uRR1E0hEJ5r9eADBZoBMVS3LGv2//PgDAxbOWobo8d9uy5hLgOADRBCz6eeWxCKqr\noygriaCpz4Bm6KiujiIYpOc7IAdQEgwCvYAgE8+YDEHg5LemopT/LnvccZHeA0QwEI0FQSR6n5RG\nS/h7gxnC38MsO7VVZaiuiCIWKkFnqhuqaqG6OgpBAaACJcEQYpEw0AoQyUJVVQQGKKEuj0VzxlES\nCgJ9gCgDVVURmIR2G6x2jb0Y9PfeUCAIZAAxSM+XJRogBIiFS4bUdaw6RZdFDWiQBB0iZNTU+Kve\nAUUBUiZkWRrwZ45GZzRgeJ7N48gPwzSxYXerJ3t4HOMYKubUl+PFDxuxp6kLC6ZV4N1tdNK28jRQ\nmQcL1VCxqXUbygKlmFM+k79OCMHMsmkFtsyPoBTEJfUX4vlDr+C9Yx/isoZVwzVcXxRFqO+++270\n9PQgFovhL3/5Czo6OvDVr3614DY1NTVob2/nP584cQLV1d42kG+99RYuuOACzzZtbW2YO3cuV1z6\nU6e7uvw73fWH6uoor7rtD41HqddJIsi7TUgR0dunoq0tToP/UxqUyjAOtdE/FCLoiCcynu1PtPVl\nddLT0dNLvcG9xglABCYGJ3m2SSYoUSKijo7OBJIZ2tGOWCJ/n6VTYpXMeD+vpzdFEyBA0NmeyFFW\nzCS9HYisMoEaasLKe8xswqhD5e/piieoumzJRZ9fAGAd2JOZDOLJFCADMAh/PaPRmDPBomP023fa\ntrgQUQcR9H7HoNq3TntvD9ra4jTP8tgOlAfKENZKfbfVVYuPh60AaGkTbW1xSCa9V/vUJNra4ujp\nTYKIBgRTgGnQE5pIpz37bW1P8HsgEdfRJsR9781EwrbVCCba2xNIqRkgBBiqc324ncSlUCd6dbQZ\ncQSEAIho4kRXH9ra4uhOJAAZEEwRpn1MKTWD4y29fFs9nR81RTIAACAASURBVHvt2b3Vm0igpbWX\nr3gk4hrahOKudzF/e8Sk8kxXLx1vR28vUAUoUAZ0X2WD2XHopMOAiPz3CDEJCLHQl0gN6DMH8mwZ\nKvEezLN5HMVj9+Eu9CY1XLJ08njnw3EMG2bVlYIQYG9jNzp709h5sBPTJ8UwuSo3qvRMwZa2HUgb\naayqW8ETy4YDq+pW4rXGd/Ba49u4ePKKgivBQ0VRo7733ntRVlYGQRBw1VVX4eabb0ZtbW6kmBsr\nV67Eyy+/DADYuXMnampqcpTo7du3Y+7cuZ5tXnrpJQDAm2++ifPOO29ABzNS6OvH8gEAoaDMLR+p\njA4LNDKvPdVB3yAaOa3Hsy0ftCiRfpYhUqKcHaXmjs1TdQO6TwEZ91AbuZYPYrer9lumZNE07pSP\nYmLzTKLx1QTVbpIy0GgzhRXGmTr3B1PLh134aGk85i0feAKKTJvS9NeiuoRZPmwP9cGeI0jqKSys\nmpd3GZedZ900YFrOOAHw5i4Zg147jR+HzBuzsOvF4LF8FOmh1g2Tk+eA6+EgCRIECJ6UD3ZOWFpL\nTyoBgKoB9PcBp625qdv51PmL9VgBo2bq/H6i4xteywezqrBIxrhKZ29hZWj5vvyY7OY3Esk/bnZd\ndcPI+56TjcE8m8dRPD7cRTumXjB//JyOY/gQCkhomBDFweO9eGvLMVjAaeGBHgrWHacdXs93FR8O\nB8JyCBfXXYC42oe1tqVkpFDUt6Aoili7di2WLl0KWXa+wIUCLS6XLl2KBQsWYM2aNSCE4Dvf+Q7+\n+Mc/IhqN4vLLLwcAtLW1obLSWUZbvXo1PvjgA1x//fVQFAX/8R//MdjjGlZwQp2nKBEAwgERHT2U\nSLV0UIJWUx5CMyPUrgIrBt30pnwQ0eBJIZagAiZyKlN54ZmgQ9dNrpK6yQ8jUUa2h9rV0tsPQSkA\nRVCQljOwDEYS+4/NIyIlebIkImMToKBPhnEhMKJmWIannTXbj0FUiIJVsCMfIQQBIYB0IG2PobDf\nNigFQEB4ysf2Dprukc8/DQASGyd0GJaXCLMYPtVM2//X+O85abW85Ex3FaYWmjnz47Zj3AxXwogb\niqBA9+RQszbtdGx9GXqsmsWuU4CP37B0T+qIn4eavaaZOs+yBoY/5YMdF/OKJ2xCPWQPdVYSSqEJ\nGiPUmqXnfc/JxmCezeMoDqpmYNPeNlSVBjFj8qmf7zuOsYU59WU40hLHS+saoUgCls+dcLKHNKro\nUxPYfXgXmtpa0ZXpxp6u/ZheOhU14er+Nx4gLq2/CG82vYeXD7+OxdULUFag58NQUBShfvrpp/HL\nX/7S42kmhGD37sJtHf/lX/7F87NbjQaA5557zvOzKIq47777ihnSqCJehEIdDkg0H1g30NxGMxPr\nqiPY0ksJNRF16FqWQq1npXy4YvMMQglPJIdQO93wNN10RbflKtRaTqIES53If9ljShQZuQ9Eo2Q5\nVCjlwx3hp1NCzTKpB9rNjhEyAxpXXyUXoSYSPR9yAQIEAIoYQEbqpePrZwwCERCWQziRbMNzB1/G\nptatUAQZs8tm9D9OS4cBr0LNElF0osI0Ld4gRRKc5imG5XdNionNs+04TKFmk45sQi0GkBRzuwCy\n65ExM1A1A5rhTHwk92TGKEyo3QWM7pSa4Vao2WerBl39SOlpiBhal0TAuX7M8lGIULsLZccqBvts\nvvfee7F161YQQnDXXXdh0aJFAGiPAPdzu6mpCXfccQeuvPJKfOtb30JjYyMMw8C//du/YdmyZbjh\nhhuQTCYRDtPn1De/+U0sXLhwBI509LH1QAcyqoHLzqkbLzwcx7BjTn0ZXtnQBN0wccGCWh5ucCZA\nMzT858af8Ng7hlV1K0bk86JKBH897XL86cALeHDro/i/S79WkNsMFkVdwU2bNg37B59K6EtSYhQN\n5Sc8IbuFaDJjoLmNLqtXV0hI2Go1RAN6OotQm9624apggH0vGoQS03BWUoVABEhEhikaNIfayk17\ncNRQL3ljsXlSgUi70kAUbXIHiBS0O/wVSNVwNQxhucQqJ2oDIz6MlBqmAd1yUi9Csq2Cyzah7kcJ\nDYoBxG1hvhg1szpUhcO9jXjp8OsAgHNqFvu2ZWdg58OEkZPBzP5AiZ1FrVkaQOhxsCQQ3SqQ8lGU\n5YOmfBguFd+NgKiACEmaww3BZUdxrlVvQuUrG0Ep6JB96HY8XX6C77XmOKkxhc7ZYKBwJVyDqpkw\noELEwO+rbPBJgqSBEAsyyf83zc+LOXYV6sE8mwfTI+DZZ59FKBTCk08+iX379uHOO+/E73//ewDA\nfffdh9mzZw/fQY0RfLizBQBw3vwzSzkcx+hglqtN+EVnmN3j7aMfoD3diRUNy7C47CyUBkpRESzj\nNsyRwGUNq9CZ7sI7R9fi4W2/wteXfGn4G5IV86b//u//9n39tttuG9bBjFX0pWy1uKBCTW0UqYyO\no7ZCrZSk+e+JqOdYPgzDaQUekSPo1JxCJt1uXOJ3gymCAtXOFOaE2uWhlgV/8mbYmceFbqKYEgUh\nAIJJKKRwV7qgK6aNEWpmJRi4Qs1IncEValmUHM84U6h9mo14x+R8bjEROV9ffCuOJ1r5z/XRyQXe\n7VbSdZjwklpm+YCkI6MZ1MMuUaKs5CFnmt3pEgDkAgo1tekQmkNtmnkbqgTFAN1flp2Bz8YlDb1J\njXuTg5LC7SQGDNvGwfzZPoTaXjHQLZ1noLvPwXCBfbZuqoinnEZDQ1WoFe6z73+Cxj3UY5hQD+bZ\nPJgeAZ/+9KfxN3/zNwBoBGp3d3fOfk8nJNIath3oQF11Ceqqx1u5DxWqodG8+hEsCDvVEAnJmNtQ\nhkRax+yGU78pS7Ho0xJ46fDrCEsh/P3SNUj1jk7jLEIIrp19NXrUOLa27cCvdv0Wtyz422EtgCxq\nT6Io8v9M08S6devOqGimeFEeavq7RFpDc1sC1WVB9OjOlw6xFWU3NN1RqKNKxFYGKenWmUKdh1AT\nwVaokUt+mNLJCB+D0zClAKFmzV0krd/mLI6HWufHxtpWh5WBFSVyhdoyPOort3zYBKiQiguAK9pA\n4bbjDCVyGDPLpvH/8rWhZuAKtaXDyiK1JfZqApE0pFWd53Mrouwi4l77gOay/Uh5vO0AfRhIoF0K\ndcPkBZE5CrUUoJM0UfeQxWCWQs3GFpYDnmNyK9R+haUBj0LtGvswe6jZvaVZOvpSGm8lP1RCzQtX\npf4JNe8gibFr+RjMs7m9vR3l5eX8Z9YjIBtPP/00Pv/5zwMAZFlGIECvyS9/+UtOrgHggQcewN/9\n3d/h29/+NtLpdM5+TkVs2tMGw7TG1WkA3ZmeIW2vmTr+Y8P9+K+PHoRpjd2uoycDt39hCf7fG8/x\n7YR4uuKlw68jpadx5dRPIhIY3VQTgQi4ef71mFE6FR+d2IaPTmwb1v0XJSt94xvf8PxsGAb+6Z/+\naVgHMpbRl9SgSAICcn7CE7L9T62dSfSlNMyqK0V7soP/ntgd7twwTMevGlHsG0swAFOCZmUgCZIv\ngQyIAUDshaabMEBn/u7leaYCZCvUumkCslGwCQdv7gL0S6gDhRRqeXAKtWk5xX6y6LQeZwSov4Yx\nbsI1EiHu7NyaMGDCGScAlCiMUKtIq47Srohen7IbTOWVkL8DJINIJF7c6qjjWQo1m+TIGciCMxlj\nKR8QdfQmVRjQ+Psll43F4+n2WQ3g9xZ0p8ujVXgyMBgEuEVGQzypcYW6mElSIciCDFh2EgwK3+O8\nUHYMK9TD8WweSI+A3/zmN9i5cyd+9rOfAQBuvPFGzJkzBw0NDfjOd76D3/zmN/jSl75U8PNGo3fA\nUGBZFjbsoROM1RfOQHWF/zL0aGWNF4ORGssfd72I327/M1bPvhQ3Lfl8UV7y7LH8Zc/raE3S83lE\nPYTldUtGZKzFjOVkYnwsQEtfG945uhY1JZW4ZslfnbSxfOvSb+DFvW9i+fSFqAw7nz/UsQxqnVbX\ndTQ2Ng7pg8cy3t9+HKmMjsuW0ZbffSmtoDoNOO3H9zbR2fzk6gja03sBUFVVFXVo2SkftuVDgOj4\nfUUdMCWoVholUsj3ARYQAyCiATVjwIAOCV7lVs6nUBsmiFJ4ed5NqPsjLwIRIMLxcwMuhXqwHmoY\nnMAERNkVhcdi3voh1C4iP1Q10w/83BIXobZfi8j2pEhSkVGNLIU6H6GmqxRigVUDBolIgECLAQ34\ne5dZVCARLA8h5h5qSUOPS6FWRAWCTYZNGJ6Uj0Ktxw1m+SAGBCIOe9EWmxjoloa+pMZjHIfqoSaE\ngFgSLMlJYMkHJ/1k7CrU2Sjm2TyYHgEAVazfeOMNPPjggzxRhCU2AcCll16KF154od8xjkbvgP6w\np7ELz689gspYENdfNouLJZZl4cnX92HnwQ7Mm1IOYhi+nzmcYxkqRmosTfFj+N2O5wEAL+x9Az19\nfVgz55qCS+TZY0npKfx+5wsIiApUQ8NT257HVGX6qBR5ngnXaDA4mWN5bMfvYZgG/mbqp9DdmUZ1\n9cD6VQwnPjFhFcwE0Jagn1/seSlEuosi1KtWrfL8AfT09OCzn/1sMZuecrAsC0+9sR+pjI5VSyZD\nlgTEUxomlDsVofu7D+GxnU/wSK/qUBUuCNDzsa+Z2jzqqkuwNkUrWCdGanGktyknz5aRKYk4ectE\nNGBpNHqtIugf7cLIRkbPcNKs+Fo+suwF9ngL+dgGQqgBQIIMTdR5xjZTPgeaQy35FfuJsuOZlook\n1G4P9RDVTD+4ixItYlBfoP0aI9TU8mHwlJWAKHObTfYkhxFYqR9vOACIggRCMlkKda7lg8F9T7gt\nH/GE6rIKOe9nCnV25J7v8VuO5UMY3Ly8IELMqw2NWq6k4bF8AIAACRbJ/bvJhrOqkKtQd/am8ZM/\nbMfNV87FlNqTpzwN5tm8cuVK/OQnP8GaNWsK9ghYvXo1/7mpqQm//e1v8etf/5pbPyzLwi233IIH\nHngAsVgM69atw6xZs4bx6IYfB4724Jl3D2LX4S7+2sFjvfjHaxaipiyEJ1/bh9c2NWNSVQm+8ukF\nJ3Gkw4ueTBxvNb+HT9Zf7KyGFoBu6nh891MwLRM3zV+DN5rexfvH1kM1NNww77p+Wz4zvNb4DhJa\nEldN/xSO9h3DRye2YVfnXiyonDPUQxrHKOLxXb9DZ6Yb31j8paKvfTa2tu3A5hPbMDXWgKU1i4Z5\nhGMDRX0TPvHEE/zfhBBEIhHEYqdnLmdXPMNzp4+1JzCpKoyMaiDqKkjc0b4b3ZkeVIUqkdJSONzb\niHNraSHicTvVo646gva9HSgLlCJqEy0tKxeaERKRyI49QdABWMhYaZTI/pW/7L1JLe0kRPikfJgk\ny/LBIvYKqHJuQh0poomGRBQQMckVareVYCDgyq/L8iEJIm9cQgSq7vdn+XATyhGxfNjjtGDAAiPU\nMh+bCAmmrCKt6ZyIBSTFQ8TdoCkfZr9xgIAdGSiY0E2Tf3a2fSco+hPqUsVuXa6kPZaPgKhwX6Oj\nUPtnXAOOV9qwDDulxoQ4AoQ6KNOxm5aOvpTKLRoxZegFYoIl8atQ6H6Sif81A4DdR7pwpDWOrfvb\nTyqhHsyzeTA9Ap5++ml0d3fjK1/5Cn/t5z//Oa677jrcfPPNCIVCmDBhwpi2Aq7f3YqfPbsTALBg\najmuWjkN63a14s3NR3HPLzZi/pRybNrbhslVJfjX689GrGRgWfpjGS8feQNvN7+Pw71NRZGilw6/\ngaN9x7Fi4nIsr12KhZXz8ODWR7GhdTM0U8etC/623330ZHrxRuM7KFWiuKT+QrQl2/HRiW146fDr\nmF8xezyK8BRBV7ob61o2wYKFt5rfxycbLh7wPra378LPd/wGiiDjutlXn7bXvqhvwlQqhWeffRZ3\n3HEHAODOO+/ErbfeOubViMHgSIsj+R9pjfOHaiTsPFxZ98P/u/RreO/oOrx4+DUYYor/XhIJKstk\ndKW7MaNsKlcBdTtZgUE37Bg7orgUap0vR5dI/t49tqyf0tI8IcKtJjrkLTtRwknPyAdWlAgAlZH+\niYJMFO7nBgCD2IR6wAq1veRKTIdQEymH8PTXMMb9uUO1B/iBE2piwBIY8XcV/wkhaMxDbTmkVWKT\nnCz7AGuOUkxjFEmgHmrDsFzquHc7930QdP07pkQQFANIBhPo7VVhBpyxaXbOskWodYeIBkRIvku7\nnjQWO/JPJMM/ceEpH5ZOLR/BJCJSpN8JVTFwK+qFJl3snvSzfMTtKM3uhJrzu9HEYJ/NA+0RcPvt\nt+P222/P2c/q1as9SvZYxhubmkEA3LFmCeZPrQAAzK4vw/RJMfzq5T2UTFfbZDp8+pBp3dSxqXUL\nAGBv1348d/BlfGZm/mvWGG/Gy0feQHmgDNfMosWnYTmEbyz5e/xs22PY0rYdj+58ol9S/cLh16Ca\nGq6ZdhUCooK66CQsrJyHHR27sb/7IGaV58/7H8fYwcbWLbDssIS/HHoF50xYPKDGKDvad+OR7Y9D\nJAK+vvhWTInVj9RQTzqKSvm4++67sWrVKv7z5z73Odxzzz0jNqiTiSOtLkLdEvdtO96e6oAsyChV\nYigNUDVIIw6hnlhZgm61BxYsVIUqnc6FJLsVuEOm3C3FmV/UL+EDcJa9U7rq63flJIuYME3Ht83a\nXhcib1HZUQCLIaSyQFMlMhrdt4nBeV3dKRhuO4NABMB0btP+iLr7c0fEQ21PRixiOBnMLpU4KIZB\nJA0Z1XBsFbLMi/ZMkkWoNaNoQi0TGUSwoOm6y7+dnUMd8P03IQQ14WoIwSS6Exm+ehEQnZQPy+Wh\nztfwxD1ZYznUxfi/Bwq20mBCR28yDaKkUBWq7Ger4uBW1AtZiKQ8vncANMoPQHc8MyxjGizOpGfz\nUNDZm8be5h7Mri/jZJph5VkT8a0bzsHq86ecdmQaAHZ27EGflsAFE89FTagKrza+hc0ntnve05Hq\nwgfHNuCxnU/gJ5v/F6Zl4ovzrs0p8v6HxbdiVtl0bGnbjsd2PQkjT9OjlsQJfHBsPWrCVVgx8Vz+\n+qemXgoAePHw6/i4cx+e2vMMvvX+9/Hjj/4Haf30SIg5nWBZFta3fASJiPjszL9GxlDxh33P5X1/\nQkviqT3P4Mk9f8Sf9r+AZw+8iP/d/isQIuAfFt9y2k+iivomNAwDy5Yt4z8vW7bMtzL8dEBjK7Vu\nEELJdV/Sm0FtWRbaUp2oClWAEIIym1BnrATY/KSuuoSr2NWhSvRptNFLditwpk5KguQU37kV6nyE\n2o6GSxtpEDk3kYEv1dvRegFbRdDztKt2QxREROQS9GmJogipYjfGSGqUWJhCrje3GEguUsfIInuN\nWBIs2B7qfhTqkFuhHhEPNZusGE5THtf5DEth2kI+k+aWj6DojaZzQzU0EFJc625G5lO6yvPLfXOo\n2b+zzlVNuAqN8WbEtV6Ph5hZPiziEOp8ed/ulBOaUlM4hnGwYPezCR1dag8IoeMfDohwxwkWUqgd\nNT4bXKHuO7mE+kx6Ng8F63efAJC/SUvDhCgaJoydFIbhxPoW2vxnVd1KXFp/Ef5z40/w+O6nkNST\naIwfxZ7OfWhLOYlUpUoMn515KeZW5K5yBEQFX1t0Cx7c+ig2n9gGAQQ3zV/jUaqTWhIPb/8lTMvE\n1TNWe343rXQK5pTPxJ6u/djTtR8A/TvrzvTgZ9t+ga8v/lLB76dxjC6a+47jWKIFS6oX4tL6i7Dl\nxA58dGIbVnTuxbyK3GZOfzn0Ct45utbzmixI+IdFt2B2+czRGvZJQ1HfhNFoFE888QTOO+88mKaJ\nd999FyUlo5sfOFo40hpHWURBNKyg+UQfehJeQp3QkkgbaVSFpgNwPMdpKwGA/ruuOoK2VDMAoCpY\nwYvTTKLDsizuH9J0w05jkB0CKhgg/Vg+mAqbMTJA0E+htv8tmFQFl71L1/09sGJK1CbU/XuoQ1IQ\n0IDuJJ00WMSxEgwEXCUlhstDzQi1CEYRgnJhos6UTYEU7vI4WLg7FjKF2h0ZVyKHgRQQ15KOQi3J\n/EvFJN7oxIyhAmL/1wRwrCUpNeNL5oEsy0cWWZwQpkkOKdINmegQ7Pfz1tqCibRqgIjUhlTo+C0Y\nUG11fSQUaoWv6ujoM2ihb014mBRq4rZ89K9QWz6EmnVP7TnJlo8z6dk8FKzb1QpRIFg2t+ZkD2VU\n0aclsL19NyaV1KIuMhGEEPzdvGvx2M4n8MTHfwBAJ+FnVc3D3PLZmFsxExPCNYUbekkBfH3xrXhw\n68+x6cRWxNU+3Lrw7xBVItANHf+7/XG0JtvwyYaLsaQ6tw39Z2auxm8/fgZTYvVYUr0Q00un4Be7\nnsSWth14dOev8eWFNw668G0cw4sNLR8BAM6tXQqBCPjCnM/iBxv+G7/b8yfcdd7tnu/YtmQH3j36\nIapClfjqWTchY2SQ1jOoCVejMlSe7yNOKxT1TXjffffhRz/6EZ588kkAtKjlvvvuG9GBnQz0JlR0\nxTNYPKMS0bCCphN92HeUxuBF7di8NpfyDIBbPvr0OAiJwrJoZN5e+31V4Up0sWB8gS6Ty5JNqA36\npSwJMlcWiagDErN8+BNaRppUQ+NFiZ7GLraSSZu/uCwfPCqt8GWPKVEcS7QUpVBHgyEgBXTY0TOW\noEMwxQF3H+LFdcTMUagFSwKjoaF+Cg2ZKh0SgyNS+MB8tRAMEMEEsbyRcVG7aC6hJVx52jI/H9nk\nTDU0m1D3PwFhxaTU6uPfDMZTlClnK9SUUJNgkhYeWrQ1OXsoEptQQ9DzFq7yNBZiQDcNEDL8GdSA\nM8EwoSNp0b+f4bJ8SANUqLN97wAQt1euevpUmKYFQTg5RTZnyrN5KGjpTOJIaxyLZlQW7HZ7OuKj\n1q0wLAPnTTyHP6eWTVgC1dDQk+nFnIqZmBKtGzCBZaT6V7uewtb2nfjBhgfwlUU34veHNmJv9wEs\nrl6Iz8zw92k3ROvwb+d6i1dvXvC3eGjbL7C9fTd+tfsp3DR/zbB2sBvHwGFaJja2bkZYCmFBJa2x\nqI9Owqq6FXir+X08s/95XDf7M/z9zx96GaZl4tPTr8CkSO3JGvZJRVGEuqKiAl/+8pcxdepUAMCu\nXbtQUVFReKNTEI22f3pKbRTRsIL3th/HzkM0+o49iDsYUba/3KNyBAQEvWocIUVCMqOjrroEHxx2\n3tfYS9VqItJYMlmiD4qMqQICJUqOh1oHLPrgy+ehZmq2STSIvPVzbg41V6htOASvMHmL2qp7qAjL\nRGmoBOgCuhM03cQSdBBr4F9abkKdo1C7btNQPwo1U+9HIuEDoMo3sQRquRAMCJb3iyhmR1Il9ST3\nKbvtOFaWh1o1maJfvOUjrdmE2iI5X4QBj+XDX6EWggnP2GXXuU+pKl01yXOPuP3WGYOSyuHukgg4\nkweL6FCFPkgYRkLtGm+h+0l05XNng1k+TMtCPKWh9CQlQpwpz+ahYN2uVgDAefPOvK6HH7ZsAgHB\nuRPO9ry+YtK5ebYoHkEpiL8/6wa8fPhN/OXQK/jPjT+FaZloiNbh5gESYlmQ8OWzbsRPtzyCja1b\nMKN0Ki6uWzHkMY5j8NjTuR89ahwXTjrPo0RfNf0K7O06gLebP0BNqBqfqF+JxngzNrZuQUN0Ms4+\nTSPxikFRd/yPf/xjPPTQQ/znhx9+GD/84Q9HbFAnC6wgsWFCFFNsP92JLlpsyAh1m50tzb7cRUFE\nVImgJ9OLaFhGSVBCeTSA9lQnQlIQJVLY01FQdxFclrqhiIqv5SOSh1B71Gyf2DxKBAj3UDMYebKL\nszGpZAIEIhS1TBMNUhW9J52AaVoggg5hMISaLcMLpqf1OEAVaob+FWr6+5FI+GAgECmhFUz6bxdi\nQapQp4ykp5uhY5XwWj5Uo7h8bcBRsTM6XZnwy3927yd7n9Uh6kEmNqFmEx8+mREMJFXWQbAfywcx\nkdY07/bDCFEQAVOAbukggaQ9/mEi1K6Cy0IWIicbPbddMitKBE5uYeKZ8mweLCzLwrpdrZAlAUtm\nDY8H/1RBS+IEjvQ2YV7lbL6SOtwQiIArp30SX1t0MwKigqpwBb626OZBpfEERAVfOetGKIKMlw6/\nQVfvxnHSsL6V2j2W157jeT0oBfG1RbcgqkTw+31/xo723fjzgZcAAFfPWH1GrywU9U24bt06/Pa3\nv+U/33///bj++utHbFAnCywyb8qEKCIhGQTg3t2oXfntFBs6KlBpIIbWxAl89VNzwEI12lOdqC2h\nXjS+DG+3jWZgjVYUUfLE5rHl/HBeD7VD0Cmx8losCCEQIMIUTM/ncYW6HwJ0ScNFWFJzVlGKIOuI\n2JtOUfIu6hDNgWcF8yg/2/IhwCE04oAUaptQj0BBIoNgSbQokeQ2NSkL0olYxkzxRA93TKFFDK+P\n3nSyqvsDmzRlDM1uqJK7TBvMk/IB0HMTIiVIBBMgggkRdrMXQrjqntQyQDC/BYWvGrjI90h41QHq\nnYeggwR0iJact0h3oJCJzP+wC3X0lFmUo092eEpVIU06BP1EA7r7MpiCk1PQdqY8mweLxtY+tHQm\nsWxuDUKBkblPxyrW2/7X87II0UhgYdU83HPBnaiujiLRndsIqVhElQhW1a3Eq41v4f1j63BJ/YXD\nOMpxMBimgbSRQcbIwF3DrFs60noaKT2NLW07UBmswPTSKTnbV4bK8bVFN+P+jx7C/+54HLqpY17F\nbN9C1jMJRT1hNE2DqqpQFPolm0gkoOuD/6MZq2hs7UMkJKMiFgAhBLWVYd6oJRKip6ot1QECgoqg\no96WKlE0xY9iyuQwQlIQ3ZkeaKbGCak7wcOtUKucUCue2DzYxDcfgXCr2RANT2oBgwgJumDwDoaA\nk0vd3xK9LEhFJyowe0gacSQyKohoQjQHr1ATwYSVVeznJq1hpTChDkshRJUIakuqC75vKBBgEz3B\ngAAvIYsF6GQiY6WdJA3BSdIgggnDtCCJjFDblo8BhIetfgAAIABJREFUEGrVUPMS6nydEhnKlEok\nzUbAIhAshwQKEGEQEymVRlcF8xBqxWUnSmkqUGRCyWBACbUBImcQIuXD5omXBRlMdA4Xo1Bn2XT6\nUhrEyuOQ6/YDxERP4uQtcZ4pz+bBYt3uM9PuoZk61rVsQlAMYlHV6HR8DMshhOUQEhhaK+nLGlbh\nnaMf4OUjb2DlpOXDkj0/DiCtp/Gr3b/Drs49vH6rPyyvX5r3uTs11oCb5q/BIzseBwBcPePKYRvr\nqYqiCPWaNWuwevVqLFy4EKZpYvv27bjppptGemyjimRaw4nuFBZMdb64p0yI4nhHEgFFhCxR8tKe\n6kB5sMyzzM2W03oyvQhJQbQm2gA4ntWg6FaoHYKru/yzXKEWdN4VsD9CzSwffp3qRCLRIkjTx0M9\njIri9NKpAAAh2onjnb30s30Ifn+QXbYDXnBnEzVWRGaZAiSxcPGMKIj47vnfHNHoJQEiiKDaXS69\n42Htx3WkuV9aFmUnr1UwYRgW7NuJ3wP9NawBsopRiQEBudt4cqh9bBvVoSoczzQCxIJoOPcBgURT\nPvTC8YQCEajHXzCpl1sZSUItgSgJEMFCVCwbtv26IwELTdDYJC/bphNPqhDClDQIsc6Tavk4E57N\ng4VpWli/uxWhgIhFM84sX/naY+vRnenBpfUXnXIxdBGlBJfUXYiXjryBd46uxWUNq/rfaBwF0avG\n8eDWR9EUP4qJ0RpEpRhCYgCKGIDgIswiERGUAgiKAZQoJTi/n9WNs2vOwpfPuhEZPYP66OSRPowx\nj6KY1bXXXoupU6eiq6sLhBBceumleOihh3DzzTcX3O7ee+/F1q1bQQjBXXfdhUWLHCXn+PHjuP32\n26FpGubPn4977rkH69atw2233ca7fM2ePRv//u//PvijGwBY/rQ7i7RhQhQf7mrlbcdVQ0OP2puT\npxhTKKHuVXtRW1KDEylKqGtsz6qbAGsuxZhbPiTF8buKOoho2ukL/g9CTxMYwYBEctNAJCKDCKqv\nQj2chLo0EEUYZUhEu3C0q8v+7IErCry4jrji6Jjlg/mrzeIq0UeqIJGBTlYMX5U4Yhcl6sQm1BZV\n2nkKBDFpfrO9HWtHXxShluj9oNmWj2wyD2R5qH32OTFSjW00hc6TdsFUd8fT7X8OuZ2IGEjrGiXU\n/aTGDBYCJD65LFOGL3aJKV6WSaBI+clGvti8eFIDCdPJo1DSg45EYtjGNlAM9tl8JmD7wQ509mZw\n8eJJXBA5E6AZGl46/AYUQcblUz5xsoczKHyy4WK81fwBXj3yFi6cdP6IP9NPZ7QlO/DTrY+gPdWB\nFROX458uvBGd9sr7cMAvGvFMRVHfhN///vfx3nvvob29HQ0NDWhqavr/23vzOCnqO///WdXH9Ex3\nz33A4MxwDseACCIIKMIoHphkjbsaMAaNxiMeyS+RRHbWDRofXhHdRONuDjXfLDFKVMySqAETgxod\nQcBwCXIjMDD3ffVVvz+qq6cbeu7u6Z6Z9/Mf6O463p+unqpXvev1eb+55ZZbulxny5YtHDt2jLVr\n13Lo0CFKSkpYu3Zt4PPHH3+cW265hcWLF/PQQw9RVlYGwOzZs3nmmWf6MaS+cSyowoeB8X9jQmI4\n/zR0ZKjr2vWLbHmLP0Pttx10iGUv3uDOhZon8LmqqJix4DN5Ucwe7ObETh+1BGezO6sDbIg+T9D+\nAt31IpyxGGHN47BrFwfrDgN02mWvK0KrZxhNS/SLoJHxVnzx4YHUxadeMu7MpwNG7XCv2q77rP1l\n9UyKyZ/ZDT0mxm+gu8or0CGQ3ZoHVfWFfTKh19+24Pa5w04szEsZAXrRGUxBx8mECUVx6bXNgURL\n5/EYkzJd/mx2tDJgwZNRMxMjl2EM2Fa6uUHrKJHoCymN19DcHshQK6rG6aaTQGwuKn05Nw8X/v7p\nSQCKZw6dzNm+mgP8ft/rfLNoGWPCeFsB/lG2mXpXA4vzFwb6JAw2kixJFOdfzFtH3uHlz19nRFIO\n7d52Wjyt1Lc3UNdeT317A5PSC7m5aGmsw41bqltreGrbczS6m7hy9KV8aczlUuM7ivRoOubOnTt5\n++23mTRpEq+//jovvvgira2tXa5TWlrKZZddBsC4ceOor6+nqUnPAvt8PrZt20Zxsd6GdNWqVeTm\n5vZnHP3mZKWeZcrL7phQV5DjwGxSyEjWfbJVZ5TMM0jxn7Tq/YK6oqUKCMpQmw0B7A3JUHv8E9KM\n7KRZtehl88zuTkvmAWdlsy1hBKxZ0R/hh2aoI2/5ABjtHA3AiXZDUPfN86ZgCpuhtvRQAA0UJsWM\nca9zpqg1qSYUnwWf2o6menUfsB8Fk+6hDpko6n9K0QPbhFFaz6t59EmFnTRUMSxG4byHo5wdjS2C\nfzeq//fi8ukiuasqKapfULd7jDkAURLUQd9tjj1yFRoCN6Td3KCFlHIMsk6VN9egmD3YVP3JUK1W\nFrHYektfzs3DgYq6VnYdqmb8qJRB1QGxvr2RNZ/9gR9+8CBHG7446/O3jrxDdVsN//vZ2sDTpGBc\nXhcbjr1Lgsk66K0SxXkXYTcnsbX8n/z5yIbARMXd1XupaKmk3efik/Lt1LbVxTrUuOXvx/9Bo7uJ\na8Yt4ctjr4hKbwahgx4pK2PCi9vtRtM0pk6dyhNPPNHlOlVVVRQVdUyGSE9Pp7KyEofDQU1NDXa7\nnccee4w9e/Ywa9Ys7rvvPgAOHjzInXfeSX19Pffccw/z58/v69h6hdHxLN3ZISSSbBb+/cbzAzVm\nOxXUCYblQ89aVbRU4rDYA6I4uGxeaF1oY0KaLkisipVWUwuo7i4rGqiKqjc7segxh2sTbVYtKIqG\nK2iCkuHpjXTd4MkZ43m3GhpUXVhY+yio1aBydBBs+fBnqLX4yFAHi+hwotbss+Ezu9A0NSTLakz8\n8/mCBXX37eANDIFslFUMZ/kA/Yar0R3etpFhS9cz5YoWcuNj8n/3bp8bE93UZ/Z7yNu90RXUwd/z\nqOTIdbgL3Gho3WWoOybK6g2Z9PfLW08BMDVlBltrPqLFUh6x2HpLX87Ng52dh6p4/s97SbKZSXcm\nkOpMYG7RCKaN7Tgvv/fpSTRg0SDJTnt8Hjad+JC3j/yVNv9Toj8f3sg9530rsMzxxpMcqj+KRTVT\n0VrF+sN/4d8mfCVkO++fLKXR1cSVBcUB+9lgJdGcyPfPv4tTzeUBX6/NbCPFmkyi2cY/yj7mlc/f\nYHvFTi7NXxDrcOMOl9fN5tPbcFodFOddHOtwhgU9UihjxozhpZdeYtasWXzzm99kzJgxNDb2biav\nFlSbRdM0ysvLWb58OaNGjeL2229n06ZNTJ48mXvuuYerrrqK48ePs3z5cjZu3Bi4aIQjLS0Jcx/9\ncVlZHZmLVpcHq8XEOaNSO12m+Qs9w16Ym09WWsf7JoeeXW+jhbT0RKrbainMGBOyroKKYvKQ5LAF\n3tf8wjEjNZmsLCc2i40Gf5vlNHtyyPpnYlIseC3+x/PWhLOWNbLQ1kS1Y3+KFwXIzkgJib+/zEhK\nwLfDjpqoZ/ntCUldxt4ZJqWjHB2aQk52CgBJCXp7cxVzYLt92X6ksJqt+O3oJFisZ3/3ig2XuQnF\na0Gl43gbNwwpqUlkZeoXO6OCRHZ6Ssh2wo0v0+2vJWvy+64tZx93ALstiaq2GkblpJNoOTvTbPY6\n8ZgbsCd0xGYxWVDw4UUX1GfGE7K+agGlLWBXSU929vp49GR5S5DXefq40TgSI+OjTE92Qq1uKekq\njiRXh68/Pd2Ow186s86n31jPyJvMzop9tNtqSUqxYLd2fNcD9fuMxLl5sPHR7tM0tbpRFPi8thUN\n+GRvBf/f9dMpGp2O2+Plg52ncCRamDUx/luNV7RU8utdayhrPo3dnMTXCr/KpxU72Vuzn2MNxylI\nzgNg04kPAbhpyjLWH36bTcc/ZHrmVLKypgPQ5mnnnWObsJlsFA8RgTnCns0Ie/hjeF7WNNZ+/kcR\n1J3wacVOWjytXF6wSGweA0SPBPVDDz1EfX09ycnJvPnmm1RXV3PHHXd0uU52djZVVVWB1xUVFWRl\n6Z7itLQ0cnNzyc/PB2Du3LkcOHCAhQsXsmSJ3q40Pz+fzMxMysvLycvL63Q/tbV9M9dnZTmprOy4\n8NQ0tOFMtIS8dyZf1OqZKVObLWQ5rw8UFCoaa9h7/Bg+zUeaJT1kGTMWvKqX6prmwPvGI7u2Zi+V\nlY0hk8TMPmuXsZg0C4rZ/2jXazprWcMmUVnXEPjMp3kxAU317VR6InfR1TQNmjLAL6jDxdMTVFQU\ntaPGcmAbHt2ZpPj09848dgOOL8gp5VXOisWs2VBMGpriQvEkBT5XUUHxUFnViNlfRs94StHc6KFS\n1ZfrbHytTfqyRoYanxp2OQsWvXtnbTtNytnlkWxaMk00hBwnRVNDtu1q8XX6HSuaqls+3Prv193e\n+bLh6OnxU3wmfe6mK5GWxnZam85+xN0XvO3+m3ufucs4Ao0lVB/lFY20+p9U1bjLwQLZ5iycvhG4\nLDV8sHcH5+dOAXo+PmPZ/tCXc/NgRtM09h6rJdVh5am75+P1aez7opZnXtvJf7+xi5Ibz+dYeSNN\nrW6WXFgQ6Eobr+yq+ozffvYKrZ425ufO4V/GXYXdkkROUhb7/3mIvxx9lzvOvYlGVxNby/9JVmIG\n07OKSE1I5qlt/83v9v6Bcbm5bDjyDz44+TFN7maWjFkcsZrt8YzT6mBi2nj21R6gurWGjAjOsxgK\nfFi2GYD5uXNiHMnwoUeCWlEUUlP1zO2Xv/zlHm14/vz5PPvssyxdupQ9e/aQnZ2Nw6H7k81mM3l5\neRw9epTRo0ezZ88err76atavX09lZSW33norlZWVVFdXk5MT/fqhmqbR2OLmnKyuH5FVtVbjsNhJ\nPMNfGtwtMTAhMSm0DrJZsdJucoeUzTM6Fxr+2eBH9N2dEIMn/oV75G5MPHT5fa6apkXN8qEoCnbv\nCFrRPX896foXjoDlQwntQGhRLeANnaQWS8xBNo9wEzATVKNhSqgPWK9a4QrxUPvO+A10hXGTZIhe\ncyeWj6vHXE5la1WnHaucahpNnAixChnWFWPbXR1Dk+HPP8OyFGmMmFSPI6LeP6NiQLhOk8GYgyrP\nBE8mblVq0FwJjEhOI9ucRzWfsbfqYEBQDyR9OTcPZk5WNdPY4mZuUQ6KomA2KUwdk8EtSybzqz99\nxk9f3UmSzYwCLDwvtvNyusKn+fjL0b/x5pF3sKhmbpqylNkjZgY+L0wbx5jkAnZW7eFk0yl2V+3F\n4/NwyTnzURWVMSkFXJZ/Ce98sYl73/wRoM+dWJR3EYvzF8ZoVAPPzOxz2Vd7gE8rd/XIM17ZUs0f\nDvyRxvZGpmVOYXrWVEY5Rg45b3FZ02kO1R9lcnphRCd0C10TNYUyc+ZMioqKWLp0KYqisGrVKtat\nW4fT6WTx4sWUlJSwcuVKNE2jsLCQ4uJiWlpaWLFiBX/7299wu908+OCDXdo9IkWbS58saHRDDIdP\n81HdWttprcUUq5Pylkoq/IL6zMYoFtWCoraFCOpAGTuTIag79m/vpEuigVmxBrq9havmYLxn+Fy9\nPi2oekbkBVCGKdcoHkGC2rdH87rlQ/dQq1qHGLSoVl1Q96G+dTQIFtSmMBM8jclqwFkeatTQSi8+\nxYNKz3zIgeNm6rpBT2HaOArTxnW6nXRrJqe8kGTq+I0FxmQ2Knd0LagVRcOtubHQeROY/mLcrFj7\n0HmzK5L8NhiTr+u4jZrb+kRS/W+nxd2C19yC0piFqiqcY8/js1Y40ngkojEK4dl3TC/NOSk/tIzi\nhUUjqKxr5Y0PjlDdANPHZZCZenY50Xhh0/F/8OaRd0i3pXHbtG+Q7zwn5HNFUbhydDH/s/M3vH3k\nrxxp+AKrycqFIzvqAl89ZjGH6o/g1lzMHTGHOSNmdjmZeCgyPWsqr+x/g+3lO0MEtaZpeHzekNcf\nlm3m9YN/xuV1oSoqx5vKeOvoX8lKzOCmKcsYk5IfiyFEhY/KtgBwkWSnB5SopvxWrFgR8nrSpEmB\n/xcUFPDyyy+HfO5wOPjFL34RzZDC0tiiiwhnUueipratHq/m7fRuLyUhmeNNZRxr1GVl9hkZaoti\nBZMnpBW4UXXDyE4GZ76TLF1fDCzBgjqMoAnpqocuqJWAoI78Yc92pPJFq+6jTuhj229TUIZaDRKt\nCSZrwEMdDwQ39bGEmZSYaErCKF2sBmWRVUWvYmI029E0LdBmvSfdwIwbLyOL3NfjONFRxKfbq8mf\n1dEm9swMdbibNINAZtwv7BMs0bnRMSxQSaREdLtOq4P2/TPIdIzodlm95raG1z8H5ESTbvtK8OiC\nLsvpRKtKoUI5RbvX1eenMwNNZz0CysvLQ87bx48f57777uPKK69k5cqVlJWVYTKZeOyxx8jLy2Pf\nvn08+OCDAEycOJGHHnooqnHv9QvqyQVn1yX/0rzRVNS18uGu01x2Qec2wVijV+L4OzaTjR/MuqfT\n0nZFGZPIc+TyaeUuABaMmkeiueO6YDFZuO/8u2NvgYshDqudiWnj2Vuzn6rWGjIT06lvb+C/d7xI\nWfNpMhPTGZGUQ5unjf11h0g0J3LzlGVMy5zMnurP2VG5m08rd/E/O1/kvpl3kdOJX3sw4fK6+fj0\nNpKtTqZlDvxTs+FMfBvMBojGFl1EJHeRoe6swoeBUenjQO0hFJSzlrOqVhRVC1gw4OwMdXDxerul\na/tJsOAJl93sENT+DLVX05uREB1Bne604avPRNPAYe5bRlFVTCiKhmLyhlg+Ek36d2HR4iPjFCyo\nzWGamiQFHbuQiiD+RiVGKUOPV0NRQ2+quiKwjMkQ1H0TsudPzOG8jOnMGN9hpzLsDR2Wjy6qfBji\n29TR6TMaJKr67yjFFNk28maTiq8uhwSSu11W9ZdyNGw6XzTotY0d6H/fqY4EvPWZ+PBR114f0Tij\nRXCPgEceeYRHHnkk8FlOTg5r1qxhzZo1/OY3v2HkyJEUFxfz5z//meTkZF5++WXuvPNOnnrqKUCv\ng11SUsIrr7xCU1MT7733XtTi9vo09h+vIzPFFjb7rCgK31wymcfvuJCi0fH7mNvwOi/Km99lnWhF\nUbhi9KWB15ecM28gwht0zMzWbwY/rdhJfXsjP/v0V5xoKmNU8giaXM3srNrD/rpDTEqbwH/M/h4X\njJiBzWzj/Jzp3DL16yyd+FWa3S38fMcLgdK3g5lPK3bS6mll7sgLZDLiABMfKb8Y0xDIUHcuqDvz\nRhsY3RKb3M1k2tLPEq2GX7XN09Gm+MwMtdPWcZGwd5OhDhbU4bJiVqMJiM+wfJxd3zmSpCcn4D5R\niKdqFCmL+tYmOmA7MHn0bLWxbWsmbVsupCArPrJOwUI2XA1wh8UObfr/1ZASeybQ/J0OQbf/mIxm\nO91nNgNl3PqZoU51JHD3tdNCt22Mw2x0SuwiQ22M34gjSoJ6pDqBnbtdjJgY2eNuNul+SbPavW/S\naGJj2HSO1utPoFLN+nkg1WHFc2os5+UUdXpuiDc66xFgzHExeOONN7jiiiuw2+2UlpZyzTXXADBv\n3jxKSkpwuVycPHkykN1etGgRpaWlXHJJdOofHymrp7nNw4zCzr9nVVHITovfCXkur4t3vtjk9zt3\nX8pselYRk9ImkGpL6bTaxXDn3KwiXv58HR+f3sbHp7dR3lLBpfkLuP3CpVRWNtLobqLJ1cwIe3bY\neSXzc+fQ0N7In49s5LkdL/C9md8+a55UXXs9+2sPkW5LY3zqmIEaWp/4sGwzCgrzcmfHOpRhhwhq\nOjLUXVk+Au3Ek8I3mDAy1ADZ9rNP+IZAaQ8qxm+UsTMESVJQibOkbjzU1iCfcri2rMb+XL4OD7Wi\n+vRuhFGYgJGebAOfCa0luc8z6wOZT0ULaeltUhW05lSsI+LjcXrIpMQwotYZVP81uE61IajbvfqT\nCbfHX3Nb63yCYTAdkxJDn2xEAssZYr0rC4olkKE2WtlHR1BbzWa05hSciZHdvtmkhvzbFUZlFqOx\ny8mmU2helQybP0PtTACfCU9D99nueKGrHgHBvPrqq7z44ouBddLT9ayvqurnkKqqKpKTO8adkZFB\nZWVl1OLedVCvGjU5P3Jt6Aeaf5RtptHVxBUFxT2qxKEqKvfOuG0AIhu8OCx2JqVN4LOazwEozruY\nr467GkVRUBSFZKuz246RV46+lLr2ev5Rtpkff/wkWYmZpCYkk2BK4HD9UU63VAD6uW7VhT8gzda3\npFG0qWqt5lD9USamjZfJiDFABDUdHupke/cZ6uzE8NmRlKA/2JwwyxgC2GjtDKApHl1Qq+EsH12f\nbIMziOHEj+FrDWSovfqkxGArRSRJd3bE3hOhEo7gx1PBmV0jo2jqQUZxIAgWsuHsNsnWDmESMoHR\n/3+3vxuhx+tDUb169Y8e3OScua9IWnc6st8e0JQut23YXPqbKe8Ok/935IiwoDYZGeqeCGpFb2Lj\n9Wm4fR6q2irRWp0kp+q/96QEM2aTSn1zezdbil+CewQYfPrpp4wdO/Yskd3VOuHeC0dfewfsPLgH\ngPkzzyEjJfb2r96WPHR5XPzto/ewmRO4fsZVOBMiN9k2lnX5zyQWsSyeOJ/PSj9nSWExN533b4Hz\naW9iuSdzOYnbrWw9uZPD9UfR/JOUEkxWZowsIsWWzKYjpWw4+VfuvfCbIeserD7KqcYKRiXnkJs8\nImySayC+l/f2vA/ApRPmdbm/4f576Yz+xiKCGmho7kGGuqUKp8XR6WTBkAx1mCy2IYCNLli+oDJ2\nfSmbF/wHa7QuD/1cf89ob65bPrxRm9iXntyRXTcEcG8xn5nNDWxPFz7xUlM2WECGE5OptmBB3fGb\nMsZkdK90e32g9vyY6FUn1IB1pycTGXtKcJZZ0UxdCvzARMx+erm7w/gdObr4u+wLVovJ/2/3vydT\nkIe6tq0WHz58rQ6cuf6nBYpCqsNKXYRqZA8EXfUIMNi0aRNz584NWaeyspJJkyYFujJmZWVRV9fR\n9rm8vJzs7O5tCX3pHeDx+thzuIqc9CR8Lk/MJ+H1ZSLgpuMfUtfWwOUFi2hr0GgjMmOIp0mJsYpl\ngm0iP567knRbGlVVTX2O5ZqCL3NNwZfx+rw0uBpp8bSSk5SFWTXj03wcqjzGB8e2MCdzdqAqyJH6\nY/zs01/i9nV0Jc5MzOD6wmsoypjY51h6i6ZpbDr0MRbVwjjb+E73J7+X8PQ0lq5Ed3wolBgTyFB3\n4qH2+DxUt9Z0aveAMwX12RlqQwCfmTFGUwKZWZtfUJtVc7cixRYkvsO1iT5rf/6yeWqUMtSJCWZs\nVn3bfc1QB9snggW1SVVD/o01wRMIw2aobXY0zZ9VD6lZrf/fmCiqWz56d5OjBrXLjmTL7+Abg+7i\n6ag2YpTvi85N2uSCNArzUik8J7KPV5OTrNxw2QSumlPQ7bKq0uGhbnLrjYs0tzVkvkWqI4H6Jhe+\nHmZoY838+fPZsGEDwFk9Agx27doVUpVp/vz5/OUvfwHg73//O3PmzMFisTB27Fi2bt0KwMaNG7n4\n4ui0OD52upHWdm/Y6h7xzOH6o/z58Eae2vYcrx/8E1bVIm2go4CiKGQkpkfMzmhSTaTZUhnlGBk4\nv6mKyr/6W72/fmA9mqZR3VrDL3f+Fo/Py1WjL+OSc+YxMW08de31/HLn/+OfFbsiEk9PONpwnIrW\nKqZnFQ278onxgmSoCZ6UGF6gVLVWo6F1OenIaXGgoHS6nJF9NsrYBR73BwkkQwTbzYndnhhslm4y\n1BZ/htrfHlqvKOELmewXadKTbZRVNfddUAdnqAnOUCsh/8aaYMtHuBsfW4IZ3BawukIrgviXdfmM\nY+LzH5Oe/xnqlh3D5xy5P19L0LZMWtdCPSC+o5yhHj0imZVfn9n9gn3gslk9m+howoSi+vB4vbhc\neuYLjzXkXJHqsOLzN4dK6cI2Fi901yMAoLKykoyMjkpFS5Ys4aOPPmLZsmVYrVYef/xxAEpKSvjR\nj36Ez+dj+vTpzJsXnUoU+74w6k/Hp3c1HP84+TEvf74O0MVYgTOPxQWX4LRGtq66MHBMSBvLjKxp\nfFq5iw9Ofsx7Jz+i0d3E1wqvYUFQFZYDtYf4n52/4YU9L/EN3/VcnXUJrZ5W9lTt41jjCS4vWBTx\n38GW09sBQhoECQOLCGr0SYk2qwlLJ76+8hb98Wi4zLOB0S2xzdMWkq02SDQyxppu+fD4H/crQYfA\nEN3dlczTt9dxB5poOftuNNGqb8sTXOWjl9nQ3pLuTKCsqrnP1ozQDHWwh7rnk8gGAms3HuoEiwnN\nY0WxukIsH8bEQ/cZGWpzmFrWnaFqJqPENQlhbqT6SnDWvfsMtb5fRdUzstHyUMcDqmIGDTw+Ly2u\nzjPUAPVN7YNCUEPXPQIA/vSnP4W8NmpPn8n48eP5/e9/H/kAz2BvJw1d4hWf5uOdL97Dopr5ZtEN\nFKaNC6khLQxerhl/NbuqPmPt/jcAWJR3UYiYBpiQNo57z7uN53a8yP9+tpZPKrfxedVhvFpHs5l/\nndDzzqa7qj7jzcMbmTNyFpecM++saiVen5dtFf/EaXEwKW1CJ1sRok18KJQY09Di6rIGdUf3w67L\nYl1RUMzVYy8PW5rH5he9rsCENMOCcbag7q6pC4SK6K4y1F5/htrr1fwNU6KXoc5M0WNKsPRtH8HC\nzBRS69lvn4iTDHV3lg+bVRfUEJq9NW4YDF+72+0F1Rty89Adwb8XawRbfodkqMOUAgwmITgzHmRZ\nGooE+94bDcvHGRnqOVNymDUpm+w0EUzR4kRFE6NHJnc5cTye2FtzgKrWas7PPo/pWVNFTA8hMhPT\nKc5fAMC0zClcO/5LYZcbk1LAd2fcjt2SxGeVBxhpz+HqMYtxWOxsOb09cB3oCk3TePf4B/xy5285\n3lTGawfWs3rbc5z0N5gy+Kzmc5rdLczKOW8dVht5AAAgAElEQVRIn4/jnaGbWuohmqbR1OImc2Tn\nnqOKQA3qzj3UAAvz5nf6WZLfouHRDA+1bvkwEZRp9p90HT3IUNutQSX2rGd7qDv2Z0yA86KoWq/s\nBb3lqgsLGJXlYGRG3+rAhtgjgicl+jPelnjJUJu7yVBbTeDR37eEWD6MKh/6MWnzelCU0ImL3RHa\nQTKSgrpDqJh76KEGfQLjUMYU9FShyW/50NzWkMoj40alcNeoyHZzFEK57ctFFETYSx9N3j/xESDN\nWIYqXxpzOeNSRlOYNj5sAs0gzzmKB+bchzPVCi36ObbV08a7xz9gV9VeZmSH9gNo9bSioJJgsuLT\nfLx6YD0fnCwl2erkxsnXs+X0NraW/5PHP/kZC0bNZX7uHHIdI9gsdo+4YNgL6pZ2D16fhjOxq5J5\nVSgoZHTSJbEndFg+/I/7vb6zJgmmJDj5twlfYWxK95OlQmtWny2oDcuH19+N0ejQaIpihjorNZFL\nzz+nz+tbOpmUWJDj5KoL85k/dWS/4osUwVaLcIJaVRRUr/79Bzd+MbLVAUHt1u0/5l54kIO95eGe\nTPSVYD+2Wel6u8FjjlYZxnghMJHU56bRrQtqm5oUN/aj4ULRmPS4qgjQFVWtNeyp3sfo5Hzyk/t+\nPhTiF5NqYmrm5B4t67Q6yLI7qWzRf7tzR17Au8c/4KNTW0IE9T8rd/PrXf8LgIKCWTXj9rkZ5RjJ\nt8/9Jmm2VIoyJjJ7xExe3reOTSc+ZNOJD8l3jqKsuZwRSdnkOUdFfrBCjxn2grqh2ahB3VXJvEoy\nEs/uftgbDAHs1fyWD48PxeTFpIVuc1HeRT3ant3a8QjRGkZYGZU/fP4MtVFZojd+3YEm2HYQLDLN\nJpXrFo6PRUhhCRafCZ3YLky+JHxAgimonKD/UZzHfyzaPfpvIVy3xc4IfsJg2HoiQXC2uzuBnzCc\nMtT+Y+b2eWjy6JaP4MY9gnAm/zj5MRoaC0bN7X5hYdiR6xhBQXIee6v3U9tWR5otlRZ3K2s/fwOz\naqYwbRztnnbavO2c48jl+sJ/CanaUZQxiVVzf8iuqs/YfGorn9Xsx6f5mDPi/Kg0bRN6TvyqqwGi\no0tieHHS4m6l0d1EXnL/7vyS/BYNj79CQ7tfVPXVguHwb0/zqZ14tv0eav8UtsD+4llQh/iN41eo\nhTTV6URQJzWOo6bZQvbMjt+NMT7DhtNmCOreZKiDjl9EM9RB4+hO4AcvG60yjPGCcQPq9nhp8jaj\neU0kJ4ofVgiP2+vmo1NbcFjszMw+N9bhCHHK3JEXcKzhOJtPb+fK0cWsP/wXGlyNfHnsFVw5+tJu\n17eoZmZmn8vM7HOpb2/kcP1RpvUwYy5Ej2H/3LIxUDIvvDgxWo53VTKvJxiC2usX1G1ufb+98c8G\nY0/Qt6f4wgsaQ/T5GJwZ6niuHBGcle6s0kaiORFv1agQa4AxPrfPf5PjF9TWPgrqzrLjfcHaTSnA\nYIIz1ENdUBvWI4/PQ0N7o7/CR3TKBAqDn20VO2h2tzAvd3bIXANBCGZWznQsqoXSU59wuP4o/zj5\nMSPsOVyWf0mvt5WS4GRG9rSo9QMQes6wF9QN/gx1cicXyQqjZF4nLcd7iuGhNjzNRnayrxlji8mE\n5jWBL/z6hkDyKaGC2hTHf3QhE/jiWPgHi2hbJ6I2wd/kJrhdeiBD7TOeUvgz1L248JpDMtSRu2AH\nZ7utateZ74Qgq8lQF9TBE0mbPM3gsQ6a0nhCdKhrr6ehvems9xtdTfz1i/dQULgo98IYRCYMFhLN\niczInkZVazW/3PlbNDSWTbxWRPEgZ9gfvcbmbjLUgZJ5XVf46A6jJJ5POUNM9fEPSFEUcCWiauHj\nNqkm0EDzWz7cXn9XuzgWqsFe8HjO7hgeak1TOrV8JITpGmkc68BEUf9voDcdD03+usgQWpmjvySE\nVC7peru2oM+jWTUmHjAucK2+FnyaD81tJSNLupANV2rb6nhky3/hw8cV+YsozrsYi8nCgdpD/GbP\ny9S7Gpg38gIyEgdHvWwhdswbeQFbTm+nyd3M/Nw5jE8dE+uQhH4ytK+GPaDDQx1e1JS3RMbyoSoq\n+NSABaPdbVgw+i4c1cPzSLKFX19RFNBM+BRdULt8Rle7+D3k1hDLR/xmPi1mM5pP9ddgDj8JxGbV\nxxL8uSGcPb5QX3t3AjaY4BuiSB7LEEHdTYbaZgmyfMTxDVokMAR1s1efoa95rGQki6Aejmiaxu/2\nvkqrpxWbOYH1h//CR2VbKMqcxPsnSlEUhX8Zd1WfHtsLw4/xqWMZYc+h1d3CNeOuinU4QgSI6tXw\n0UcfZceOHSiKQklJCeee2zFJ49SpU3z/+9/H7XYzZcoUfvzjH3e7TjQw2o531jCgoqUKq2oJ2/2w\n1/jMaGpohro/j3gWTB1NorVz4an4TGh+Qe32C+relGgbaBJCfLzxK9RMqgLdCWp/c5vgZjTGDYPR\nbMftb/KT0AtBbVEt4AV8akRndPfEFx7u82iWYYwHAhlqb0cN6owUEdTDkQ/LNrOv9gBFGZNYseA2\n1mz9I5tOfMh7Jz4iLSGVW6bewNiU0bEOUxgkKIrC92d+G5/mI8nSt94NQnwRNdWyZcsWjh07xtq1\nazl06BAlJSWsXbs28Pnjjz/OLbfcwuLFi3nooYcoKyvjxIkTXa4TDYxJicGNGgx8mo+Klkqyk7K6\nLN7eUxSfGc3vaTYEdW8mpJ3J9Yu6LiWnECSo/ZaPeBaqIRPj4tjyYTYp4DOhaUqgi+OZ2MJYPozx\nGe1nXf561L2p1hG4IdIiO/0huASfYU/qjGBBHc8WokhgPClp1fyeWclQD0uqW2tYd/DPJJoTuWHS\nv2K3JvGvE77MvNzZ7Kr6jHm5s3vUkEsQgrGLkB5SRG1SYmlpKZdddhkA48aNo76+nqYm/aLk8/nY\ntm0bxcXFAKxatYrc3Nwu14kWDS1u7DZz2EYNB+uO4PK5yY9QsXRFM6OpZ1TdiGLGWNE6BLXR5jSu\nBXVw6TZT/MZpUlW8DRn4GjIwqeH/hMJNSuwQ1Eb3Sv030F1GOJjA8Ytw/efgpwPdCfzQBjzxe5wi\ngTHWDkGdQKqj6xsOYWjh03z8bu+rtHtdXDfhK6QmdHTFHGnP4fKCRSKmBUGInqCuqqoiLa1jYkZ6\nejqVlbofuaamBrvdzmOPPcayZct46qmnul0nWjS2uDqdkPjxqa0AzB5xfkT2pWoWUD1omobL1/sJ\nab1F0cyg+jPU/g6N8Zz5TRgsgtqk4D58Lp4j01A7sXzMmpjN7MnZjBnZYRWymg3Lh3FM9N9AbzLU\nRqWQSDdUUVVV94UTOukwfAyDoxpLJDD+Xtr8gtphsXd6zIWhycentrK/7hDTMidLa2dBEDplwK6G\nmqaF/L+8vJzly5czatQobr/9djZt2tTlOp2RlpaE2dw3cZGe4aCp1U1ejpOsLGfIZ23uNv5ZtZsc\neyYXTpgWEcuHCQseBVLTbRi6NsVuP2vfkcKkmHGrPjIyHJjMQHt099df2sytgf9npJx9TIKJ9RhU\nRRehncWRleVk1rTckPdyXKn+lX36eqoPgOy05LO209l2k+1J0KyXq4v4d+BT9djSU7vcdvDfZaI1\noU9xxPr49ZQUh/5Ith29S2KmI6VHsQ+W8Qld4/Z5eOvIX7GoZpZOvFY60QmC0ClRE9TZ2dlUVVUF\nXldUVJCVpVfKSEtLIzc3l/z8fADmzp3LgQMHulynM2prW/oUX1aWk6Nf1KBpkGgxUVnZGPJ56amt\ntHvamZW3gOqq5j7t40wUf5vxE+XVNLTocXtc2ln7jgRZWU4UzYSiaJw8VUtTa5u+v3ZfVPYXCVr8\nfnaA9hZvp3FmZTljPgZVVTGpSq/iaGnSnxK4vW4qKxtpc7dDArjbQn8DXY3Pb71H8Z39m+0vetbb\ng7etB79Jv/jWvL37DiA+jl9PcbfrNz1GdZ6UBEe3sfdmfCK845uPyrZQ215Hcd7FIVYPQRCEM4ma\n5WP+/Pls2LABgD179pCdnY3D4QDAbDaTl5fH0aNHA5+PGTOmy3WiQaBLYpgKH5v9do85EbJ7AJg0\nPS3d6mkLVN3oTYWHXu/P33Sj1d2OZxBYPgaLhxp020dnFT46w/Ap+/y1wY0W5ImWnv8GDIuQEo2G\nKv6JjjZLDzzC/mWHeiOCM+ccZDtFVA0XXF43G46+i1W1cHnBoliHIwhCnBO1q+HMmTMpKipi6dKl\nKIrCqlWrWLduHU6nk8WLF1NSUsLKlSvRNI3CwkKKi4tRVfWsdaKJ0SXReUaFj6rWag7UHaYwdRwZ\niekR25/JX3O6xdUWmCQYTUFt1AhudbsCvt1oerb7S/DEuIQ4jhPArCq99tIm+Os3e/2C2hsQ1D2f\n5GYcv6h0KPT7su09iEfRTGh4um1TPtgJvsnTvCay0qN3gy/EFx+Wbabe1cDi/IU4rXLcBUHomqim\nl1asWBHyetKkSYH/FxQU8PLLL3e7TjRp7KQG9eZT2wC4cOSsiO7PjH5xbna1BzLUnXXaiwRGF7s2\nj0vPhiodE+PiEVNQM5dofi+RwNQHQR3osGgIar+NINHac0GdEBDUkT+OiqaiAUmW7svC6YKaIS+o\ngzPUmttK5hApmdfbHgGvvvoq69evDyyze/duPv30U77xjW/Q0tJCUpLuNb///vuZOnXqgI8n0ri8\nLjYce5cEk1UatQiC0CPiV10NAA2BtuP+zKHPyztfbGLDsb+TYLIyPSuyFwYL+sX4QN3BwOP+3lR4\n6C1GBYY2t0vPhiq968o30KiKXmlCUX1xL6jNZhW1lxOUzCYTmk8NWD4MX253VTWCMdqzRyNDrfqs\neDxmEqzdf/eG5cQ6xC0fwd078VhJHwKCui89Aq677jquu+66wPpvv/12YPnHHnuMwsLCAR9HNHn/\nZCmNriauLCjGYZWSeIIgdM/Qvhp2Q6BLYpKVqtYant+9huONJ0mxJrN8ytewmSNbbzbTO57Trt38\n7cTfSdIyQOldDeLeYlhM2jyugL0gni0foGdJwUdCHGfSAb4yfwy9nfBvMin+9vOhGereZHmN0oLR\nENRJVTOoamrAckn3UytUzYSX+PbkR4LgvxfNPTSaunRW79/hcAR6BDz99NMAYW13zz33HKtXrx7Q\nmAeS8uYKNhx9F5vJRnH+gliHIwjCICG+VUuUMTLUKQ4rbx95k+ONJ5kz4nz+bcKXo9IK1GZKwnVw\nOrYpn9Ck6PW1o5mhTlASAfisdk/AQ50Q55lfY7JbvMe5YHpu9wudgaoo4DXjNbfi03x6hlrr3cQ+\np8WJpoGVyP8+rd4UfE0mLObuBXUgQz3UBXXQ79Cs2QINewYzVVVVFBUVBV4b9f4dDkdIj4A9e/Yw\na9Ys7rvvvsCyO3fuZOTIkSHVl5555hlqa2sZN24cJSUl2GyD96ajvr2Bn+94gRZPKzdOuk462QmC\n0GOGuaDWfczJdiuVrVUoKHx90r+FeHkjidmk4GtK55IRi9h0+l0guoL6HHUKR9v2UFr1IVZVn1TT\nG3tBTDAEdbzH2Ue0xgx8mSf5ovGE3oZeM/Wqtm1KgoP2XReRO7Yg4rGZ/G3Ue1K9RB0mgjq42kyi\naWiKq570CFi4cCEAr732Gl/96lcDyy9fvpyJEyeSn5/PqlWreOmll7j11lu73F9/egdEs8xgi7uV\nn7z7/6hpq+X6qV/iK0XFMYult0gs4ZFYwiOxhKe/sQxrQV3f7MJsUkhKMFPTVkdKQnLUxDQQaG8+\nO30e20/sp55ynAnR8+dlO1Nw7TmPxCmbcal6p7d4z/wqmgmfBpY+XnDjnoYcyDzJ7qq9aIoXxde7\ncZrNKlqbI+CljiTZqYm0tnt6JPCNSZFDXVAH/704hkilh770CDAE9ebNm3nggQcC6y5evDjw/+Li\nYt56661u99+f3gGRql/e4m7hmU9/RaO7mYLkPEY789hbs59jdSe4KHcOC7Iu7nJf8VRLXWIJj8QS\nHoklPD2NpSvRHbU61IOBhma97biGRr2rgXRbalT3Z/Y/Sm9ocWM+Poe2f16CrRc1iHvLwhm55Cbm\n0n5sYuC9+BfUKmgqJnVo/jTV5mzQVHYZglrr3T1tVmoiV87O55Lzem856Y7bvzKFh26Z3aNlTYp+\nIxDNOQDxQEJQhjolYWgI6r70CAAoLy/HbrdjterHXNM0br75ZhoaGgBdbE+YMGGAR9N7vD4vL+75\nPcebymj3uthRuZv/O/w2++sOcW5mEV+b+FXpiCgIQq8ZthlqTdOob3YxKstOfXsDPs1HWkKUBbX/\nkfp/v7GbdreXGRNGnFUDO5JYzCZu+/IUHv5tMx57A2piY9wLIHN7Jp72+I6xP1hVK+7mDE4oZWhm\nFcXTuycUqqJwffH4qMRmUlVMPbyPMQ2TDHWwhzojKTmGkUSOvvQIAKisrCQ9vaMuv6IoXH/99dx8\n880kJiaSk5PDvffeG6th9Zg/HnqLvTX7mZoxiTvOvZn69gaONRynrr2BebmzUZWheTMvCEJ0GbaC\nuqXNg8frI8Vupba9HoC0aGeo/WrF5fbylfmj/ZUiopsJyc9xcu2Ccby6CUDDclV8WylSa2dRUde3\nR8KDgRkTMvmgLBOroxJF9UWlnvRAYGSobXH+xKO/BFcxyXIMnS6JfekRMHXqVJ5//vmQ95YsWcKS\nJUuiE2QU+PjUVt49/gE5SdncXLQMVVFJs6VG/dwvCMLQZ3BezSNAXVM7oE9IrG2rA4h6hnpKQTo7\nD1VzzcVjmDomI6r7CuaK2fl8dqyWsqrmHlVwiCXXXjI20HBnKLJkbgEf/OYwsBeIUsfDASBLHU1F\nfQ25yVndLzyIMSsdx2dkaloMIxH6yxcNJ3h53+skmhO589ybSDQnxjokQRCGEMNXUDfqglrPUFcA\n0c9Qjz8nhQeWR7b7Yk9QVYXvXTcdj9fX62YkA820sQN3oxELMlMSuXjSWEpbtqImNQ3aDPVN8xby\nxenzyUmNnxna0UBRlECzoXPS0rtfQYhb3jj4Jh7Ny+1Fy8hOGto3goIgDDzxna6MIoagTk4KylDb\nhs4j3TNRVQWrZXBmQ4caS+YWoNXnAGBicFomkpOsTB3iNz8GiqaieU2kO4Zm2bzhwMG6I+yvO8Tk\n9EKKMiZ1v4IgCEIvGcaCug0ItXykJ8gjXSH6ZKYkMi1zMgDmQSqohxOKZsbsS5TKD4OYt4/8FYAl\nYy6LcSSCIAxVBufz5ghQ2xjkoa6ow6KapSuWMGBcP2cWD/3pKJMKJsc6FKEblk28dshXMxnKHK4/\nyr7aA0xKm8DYlNGxDkcQhCHKsBXUxqTEFLuVmrY60mypkoESBoys1CSeXvr1uJ8kKsD8gvNiHYLQ\nD97yZ6evkuy0IAhRZNhezQ0PdaJNocndHPUKH4JwJlZL79qOC4LQO47UH2NvzX4K08YzPnVMrMMR\nBGEIM6wFtUlVcCnNQPQrfAiCIAgDy1tH/d7p0ZKdFgQhugxbQV3b1E6y3Uqd0dRFMtSCIAhDhrKm\n03xW/TnjU8cwIW1srMMRBGGIE1UP9aOPPsqOHTtQFIWSkhLOPffcwGfFxcWMGDECk0kv5bZ69WqO\nHj3Kd7/7XSZMmABAYWEh//mf/xnxuDRNo66xnZHpSdS0+yt8SIZaEARhyPDeiQ8BKM67OMaRCIIw\nHIiaoN6yZQvHjh1j7dq1HDp0iJKSEtauXRuyzK9//Wvsdnvg9dGjR5k9ezbPPPNMtMICoM3lxeX2\nkuKwUtdWCUiGWhAEYajQ4m5hy+ntpNvSmJY5JdbhCIIwDIia5aO0tJTLLtN9a+PGjaO+vp6mpqZo\n7a5XNDTrra2Tk/QKHyAeakEQhKHCR6c+weVzs2DUXFRl2DobBUEYQKJ2pqmqqiItraNRSnp6OpWV\nlSHLrFq1imXLlrF69Wo0TQPg4MGD3HnnnSxbtowPP/wwKrHVG4LabqW2XQS1IAjCUMGn+Xj/RCkW\n1cK83NmxDkcQhGHCgNWhNgSzwXe+8x0uvvhiUlJSuPvuu9mwYQMzZszgnnvu4aqrruL48eMsX76c\njRs3YrVaO91uWloSZnPvWmrvP9UIQG6Ok/3NDTisds4ZMfTaKGdlOWMdQtQYymMDGd9gZ6iPL57Z\nXbWX6rYa5ufOlmZdgiAMGFET1NnZ2VRVVQVeV1RUkJWVFXh9zTXXBP6/YMEC9u/fz5VXXsmSJUsA\nyM/PJzMzk/LycvLy8jrdT21tS69jO3FKr+yh+LxUNdeQmZhBZWVjr7cTz2RlOYfcmAyG8thAxjfY\n6c34RHhHnvdOfATAJefMj3EkgiAMJ6Jm+Zg/fz4bNmwAYM+ePWRnZ+NwOABobGzk1ltvxeXSrRef\nfPIJEyZMYP369bzwwgsAVFZWUl1dTU5OTsRjMzzUtkSNdq9LKnwIgiAMQmraanl+1xpWb32O53et\n4ZXP32Bf7QEmpI5llGNkrMMTBGEYEbUM9cyZMykqKmLp0qUoisKqVatYt24dTqeTxYsXs2DBAr72\nta+RkJDAlClTuPLKK2lubmbFihX87W9/w+128+CDD3Zp9+grhqDG0gZIhQ9BEITBhKZpbDm9nT/s\n/z/avG0oKGh02AoXSak8QRAGmKh6qFesWBHyetKkSYH/33TTTdx0000hnzscDn7xi19EMySgY1Ki\nxyRdEgVBGH501SPg1KlTfP/738ftdjNlyhR+/OMfs3nz5rA9Ak6dOsUPf/hDvF4vWVlZPPnkk1FJ\nggTT6mnjd3v/wD8rd5NgsvL1Sddx4cjzaXQ1U99ej1fzMialIKoxCIIgnMmATUqMJxqaXZhUhUav\n7qVOlwy1IAjDhO56BDz++OPccsstLF68mIceeoiysjKAsD0CnnnmGW644Qauuuoqnn76aV577TVu\nuOGGqMa/8djf+WflbsaljGH5lK+RmZgOQEqCk5QE8aQLghAbhmWBzvpmFymOBA7XHwWgIDk/tgEJ\ngiAMEF31CPD5fGzbto3i4mJAL22am5vb6bY2b97MpZdeCsCiRYsoLS2NcvSwo3IPVtXCPed9KyCm\nBUEQYs2wFNQNLS5SnFYO1B4iNSFFTsqCIAwbuuoRUFNTg91u57HHHmPZsmU89dRTgeXC9QhobW0N\nWDwyMjLO6jUQacpbKilvqWByeiFWkyWq+xIEQegNw87y0eby4HL7sKe0cdrdzAU5M1EUJdZhCYIg\nxITgHgGaplFeXs7y5csZNWoUt99+O5s2bWLy5MlhewR0tp2u6EvvAIPDrQcBmD/2/JiXHIz1/oOR\nWMIjsYRHYglPf2MZdoLaqPDhs1cDUJg2NpbhCIIgDChd9QhIS0sjNzeX/HzdBjd37lwOHDjAwoUL\nw/YISEpKoq2tDZvNRnl5OdnZ2d3uvy+9A0C/2H109FMUFAqsY2JayzyeaqlLLOGRWMIjsYSnp7F0\nJbqHneXDyKG4EioAmJA6LnbBCIIgDDBd9Qgwm83k5eVx9OjRwOdjxozptEfAvHnzAtvauHEjF18c\nvXJ19W0NHKk/xtiU0Tis9qjtRxAEoS8Muwx1TloSq26exS8O/UP804IgDDu66xFQUlLCypUr0TSN\nwsJCiouLaWlpCdsj4N577+X+++9n7dq15ObmhnTAjTTbynajoXFu1pSo7UMQBKGvDDtBDWB1ttLQ\n3iT+aUEQhiVd9QgoKCjg5ZdfDvm8sx4B2dnZ/OY3v4lOkGew9eQOAM7NLBqQ/QmCIPSGYWf5ADhQ\newgQ/7QgCMJgwOV1sbN8LyPsOWQnZcY6HEEQhLMYloJ6f91hQPzTgiAIg4G9NQdwed2cmyl2D0EQ\n4pNhJ6g1TeNA7SEyEtPEPy0IgjAI2Fm1BxC7hyAI8cuwE9TVbTU0uZuZkj1B/NOCIAiDgH01B0iz\npVCQfE6sQxEEQQjLsJuUmGxN5pJz5nP1xIXgiXU0giAIQncsPGc+Y3NGoSrDLgckCMIgYdidnawm\nC9cX/guj0yTTIQiCMBhYXLCQC/NmxjoMQRCEThl2gloQBEEQBEEQIokIakEQBEEQBEHoByKoBUEQ\nBEEQBKEfRHVS4qOPPsqOHTtQFIWSkhLOPffcwGfFxcWMGDECk8kEwOrVq8nJyelyHUEQBEEQBEGI\nN6ImqLds2cKxY8dYu3Ythw4doqSkhLVr14Ys8+tf/xq73d6rdQRBEARBEAQhnoia5aO0tJTLLrsM\ngHHjxlFfX09TU1PE1xEEQRAEQRCEWBI1QV1VVUVaWlrgdXp6OpWVlSHLrFq1imXLlrF69Wo0TevR\nOoIgCIIgCIIQTwxYYxdN00Jef+c73+Hiiy8mJSWFu+++mw0bNnS7Tjiyspx9jqk/6w4GhvL4hvLY\nQMY32Bnq4+sPQ+WcLbGER2IJj8QSnqEUS9QEdXZ2NlVVVYHXFRUVZGVlBV5fc801gf8vWLCA/fv3\nd7uOIAiCIAiCIMQbUbN8zJ8/P5B13rNnD9nZ2TgcDgAaGxu59dZbcblcAHzyySdMmDChy3UEQRAE\nQRAEIR6JWoZ65syZFBUVsXTpUhRFYdWqVaxbtw6n08nixYtZsGABX/va10hISGDKlClceeWVKIpy\n1jqCIAiCIAiCEM8oWk+MyoIgCIIgCIIghEU6JQqCIAiCIAhCPxBBLQiCIAiCIAj9YMDK5sULQ7G1\n+U9+8hO2bduGx+PhjjvuYNq0afzwhz/E6/WSlZXFk08+idVqjXWY/aKtrY0vfelL3HXXXcydO3dI\njW/9+vU8//zzmM1mvvOd7zBx4sQhM77m5mbuv/9+6uvrcbvd3H333WRlZfHggw8CMHHiRB566KHY\nBtkH9u/fz1133cXNN9/MjTfeyKlTp8Ies/Xr1/Pb3/4WVVW5/vrrue6662Id+qAjHs7ZPT3eA0G8\nnO9bW1tZuXIl1dXVtLe3c9dddzFp0qSYnrvi4TqxefNmvvvd7zJhwgQACgsL+da3vhWz7yVeri+v\nvvoq69evD7zevXs3L7/8ckyuBVG7Lu78azoAAAkeSURBVGnDiM2bN2u33367pmmadvDgQe3666+P\ncUT9p7S0VPvWt76laZqm1dTUaJdccom2cuVK7a233tI0TdOeeuop7aWXXopliBHh6aef1q699lrt\n9ddfH1Ljq6mp0S6//HKtsbFRKy8v1x544IEhNb41a9Zoq1ev1jRN006fPq1dccUV2o033qjt2LFD\n0zRN+/73v69t2rQpliH2mubmZu3GG2/UHnjgAW3NmjWapmlhj1lzc7N2+eWXaw0NDVpra6t29dVX\na7W1tbEMfdARD+fsnh7vgSCezvdvvvmm9qtf/UrTNE07ceKEdvnll8f83BUP14mPP/5Yu/fee0Pe\ni1Us8Xp92bx5s/bggw/G7FoQrevSsLJ8DMXW5hdccAE/+9nPAEhOTqa1tZXNmzdz6aWXArBo0SJK\nS0tjGWK/OXToEAcPHmThwoUAQ2p8paWlzJ07F4fDQXZ2Ng8//PCQGl9aWhp1dXUANDQ0kJqaysmT\nJwNZxsE4PqvVyq9//Wuys7MD74U7Zjt27GDatGk4nU5sNhszZ85k+/btsQp7UBIP5+yeHu+BIJ7O\n90uWLOG2224D4NSpU+Tk5MT03BXP14lYxRKv15fnnnuO2267LWbXgmhdl4aVoB6Krc1NJhNJSUkA\nvPbaayxYsIDW1tbAI5yMjIxBP8YnnniClStXBl4PpfGdOHGCtrY27rzzTm644QZKS0uH1Piuvvpq\nysrKWLx4MTfeeCM//OEPSU5ODnw+GMdnNpux2Wwh74U7ZlVVVaSnpweWGQrnm4EmHs7ZPT3eA0E8\nnu+XLl3KihUrKCkpiWks8XSdOHjwIHfeeSfLli3jww8/jFks8Xh92blzJyNHjsRkMsXsWhCt69Kw\n81AHow2hioF//etfee2113jxxRe5/PLLA+8P9jH+8Y9/5LzzziMvLy/s54N9fAB1dXX8/Oc/p6ys\njOXLl4eMabCP7//+7//Izc3lhRdeYN++fdx99904nR3tXQf7+MLR2ZiG4lgHmnj8DmMRUzyd7195\n5RX27t3LD37wg5idu+LpOjF69GjuuecerrrqKo4fP87y5cvxer0xiQXi7/ry2muv8dWvfvWs9wcy\nlmhdl4aVoB6qrc0/+OADfvGLX/D888/jdDpJSkqira0Nm81GeXl5yKPKwcamTZs4fvw4mzZt4vTp\n01it1iE1voyMDGbMmIHZbCY/Px+73Y7JZBoy49u+fTsXXXQRAJMmTaK9vR2PxxP4fLCPzyDcbzLc\n+ea8886LYZSDj3g9Z8fyHBQv5/vdu3eTkZHByJEjmTx5Ml6vF7vdHpNY4uk6kZOTw5IlSwDIz88n\nMzOTXbt2xSSWeLy+bN68mQceeABFUQK2CxjYa0G0rkvDyvIxFFubNzY28pOf/IRf/vKXpKamAjBv\n3rzAODdu3MjFF18cyxD7xU9/+lNef/11/vCHP3Dddddx1113DanxXXTRRXz88cf4fD5qa2tpaWkZ\nUuMrKChgx44dAJw8eRK73c64cePYunUrMPjHZxDumE2fPp1du3bR0NBAc3Mz27dvZ9asWTGOdHAR\nr+fsWP2NxtP5fuvWrbz44ouAbs2J5bkrnq4T69ev54UXXgCgsrKS6upqrr322pjEEm/Xl/Lycux2\nO1arFYvFwtixY2NyLYjWdWnYdUpcvXo1W7duDbQ2nzRpUqxD6hdr167l2WefZcyYMYH3Hn/8cR54\n4AHa29vJzc3lsccew2KxxDDKyPDss88yatQoLrroIu6///4hM75XXnmF1157DYBvf/vbTJs2bciM\nr7m5mZKSEqqrq/F4PHz3u98lKyuLH/3oR/h8PqZPn86///u/xzrMXrF7926eeOIJTp48idlsJicn\nh9WrV7Ny5cqzjtlf/vIXXnjhBRRF4cYbb+QrX/lKrMMfdMT6nN2b4x1t4ul839bWxn/8x39w6tQp\n2trauOeee5g6dWrMz12xvk40NTWxYsUKGhoacLvd3HPPPUyePDlm30s8XV92797NT3/6U55//nlA\n95rH4loQrevSsBPUgiAIgiAIghBJhpXlQxAEQRAEQRAijQhqQRAEQRAEQegHIqgFQRAEQRAEoR+I\noBYEQRAEQRCEfiCCWhAEQRAEQRD6gQhqQegn69atY8WKFbEOQxAEQegBcs4WooEIakEQBEEQBEHo\nB8Oq9bgwvFmzZg1vv/02Xq+XsWPH8q1vfYs77riDBQsWsG/fPgD+67/+i5ycHDZt2sRzzz2HzWYj\nMTGRhx9+mJycHHbs2MGjjz6KxWIhJSWFJ554Augo5n/o0CFyc3P5+c9/jqIosRyuIAjCoEbO2cJg\nQjLUwrBg586dvPPOO7z00kusXbsWp9PJRx99xPHjx7n22mv5/e9/z+zZs3nxxRdpbW3lgQce4Nln\nn2XNmjUsWLCAn/70pwD84Ac/4OGHH+Z3v/sdF1xwAe+99x6gd3x6+OGHWbduHQcOHGDPnj2xHK4g\nCMKgRs7ZwmBDMtTCsGDz5s188cUXLF++HICWlhbKy8tJTU1l6tSpAMycOZPf/va3HD16lIyMDEaM\nGAHA7NmzeeWVV6ipqaGhoYHCwkIAbr75ZkD3402bNo3ExEQAcnJyaGxsHOARCoIgDB3knC0MNkRQ\nC8MCq9VKcXExP/rRjwLvnThxgmuvvTbwWtM0FEU567Ff8PuapoXdvslkOmsdQRAEoW/IOVsYbIjl\nQxgWzJw5k/fff5/m5mYAXnrpJSorK6mvr+ezzz4DYPv27UycOJHRo0dTXV1NWVkZAKWlpUyfPp20\ntDRSU1PZuXMnAC+++CIvvfRSbAYkCIIwhJFztjDYkAy1MCyYNm0aX//61/nGN75BQkIC2dnZzJkz\nh5ycHNatW8fjjz+Opmk8/fTT2Gw2HnnkEb73ve9htVpJSkrikUceAeDJJ5/k0UcfxWw243Q6efLJ\nJ9m4cWOMRycIgjC0kHO2MNhQNHnOIQxTTpw4wQ033MD7778f61AEQRCEbpBzthDPiOVDEARBEARB\nEPqBZKgFQRAEQRAEoR9IhloQBEEQBEEQ+oEIakEQBEEQBEHoByKoBUEQBEEQBKEfiKAWBEEQBEEQ\nhH4ggloQBEEQBEEQ+oEIakEQBEEQBEHoB/8/8g/a1wP6MPIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe06efa65c0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "individual_loss_rec = loss_rec\n",
    "individual_accuracy_rec = accuracy_rec\n",
    "individual_loss_smooth = smoothing(loss_rec, smoothingRadius)\n",
    "individual_accuracy_smooth = smoothing(accuracy_rec, smoothingRadius)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('First training Training')\n",
    "plt.subplot(221)\n",
    "handles = plt.plot(population_loss_rec)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(222)\n",
    "handles = plt.plot(population_loss_smooth)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(223)\n",
    "handles = plt.plot(population_accuracy_rec)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(224)\n",
    "handles = plt.plot(population_accuracy_smooth)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('Classifier re-training Training')\n",
    "plt.subplot(221)\n",
    "handles = plt.plot(individual_loss_rec)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(222)\n",
    "handles = plt.plot(individual_loss_smooth)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(223)\n",
    "handles = plt.plot(individual_accuracy_rec)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(224)\n",
    "handles = plt.plot(individual_accuracy_smooth)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "TNVHgLYgErpv"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(221)\n",
    "handles = plt.plot(population_loss_rec)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(222)\n",
    "handles = plt.plot(population_loss_smooth)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(223)\n",
    "handles = plt.plot(population_accuracy_rec)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.subplot(224)\n",
    "handles = plt.plot(population_accuracy_smooth)\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(handles, ['train', 'test'])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "2 - Two-Classes Classification (BNCI) Colab.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
